Patent Publication Number: US-2022230376-A1

Title: Motion prediction using one or more neural networks

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This is a 371 National Phase of PCT Application No. PCT/US2020/033148, filed on May 15, 2020, which claims priority to U.S. Provisional Patent Application Ser. No. 62/849,731, filed May 17, 2019, and entitled “Adversarial Training of Deep Motion Prediction Controllers,” which is incorporated herein by reference in its entirety and for all purposes. 
    
    
     BACKGROUND 
     People are consuming an ever-increasing amount of digital content using devices with ever-increasing display capabilities. Accordingly, it is desirable to improve the quality of this digital content as well, such as by increasing a perceptual quality of computer animation to be displayed. Machine learning has been introduced to help improve such quality of generated animation, but existing approaches often utilize techniques that can lead to overfitting and an inability of a network to correct for its own accumulated mistakes, which can negatively impact the quality of the generated animation. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       Various embodiments in accordance with the present disclosure will be described with reference to the drawings, in which: 
         FIGS. 1A, 1B, 1C, and 1D  illustrate frames of animation that can be generated, according to at least one embodiment; 
         FIG. 2  illustrates a content presentation architecture that can be analyzed, according to at least one embodiment; 
         FIG. 3  illustrates an example training architecture, according to at least one embodiment; 
         FIGS. 4A and 4B  illustrate performance data for different models, according to at least one embodiment; 
         FIG. 5  illustrates an example process  500  for training a motion prediction model, according to at least one embodiment; 
         FIG. 6  illustrates an example process  600  for generating character state for a frame, according to at least one embodiment; 
         FIG. 7A  illustrates inference and/or training logic, according to at least one embodiment; 
         FIG. 7B  illustrates inference and/or training logic, according to at least one embodiment; 
         FIG. 8  illustrates an example data center system, according to at least one embodiment; 
         FIG. 9  illustrates a computer system, according to at least one embodiment; 
         FIG. 10  illustrates a computer system, according to at least one embodiment; 
         FIG. 11  illustrates at least portions of a graphics processor, according to one or more embodiments; 
         FIG. 12  illustrates at least portions of a graphics processor, according to one or more embodiments; 
         FIG. 13  is an example data flow diagram for an advanced computing pipeline, in accordance with at least one embodiment; 
         FIG. 14  is a system diagram for an example system for training, adapting, instantiating and deploying machine learning models in an advanced computing pipeline, in accordance with at least one embodiment; and 
         FIGS. 15A and 15B  illustrate a data flow diagram for a process to train a machine learning model, as well as a client-server architecture to enhance annotation tools with pre-trained annotation models, in accordance with at least one embodiment. 
     
    
    
     DETAILED DESCRIPTION 
     In applications such as gaming and animation, it can be desirable to generate or render motion of one or more virtual characters or objects. In some instances this can involve providing a virtual character, such as illustrated in the example animation frames  100  of  FIG. 1A-1D , where that character can be comprised of a number of elements connected by a number of joints. In order to animate this character, pose or state information can be changed for these various joints in order to cause the corresponding elements (e.g., arms, legs, or head) of this character to move or change orientation and position appropriately. There can be various ways to provide input to adjust the pose or state of these various joints, as may involve manual input or motion capture, among other such options.  FIGS. 1A through 1D  illustrate example frames of a sequence of animation wherein this character is animated to appear to climb an incline. In order to properly render the animation, there can be various constraints applied, as may relate to limitations on poses of the various joints, as well as physical constraints due to the surface of the incline being climbed. 
     Various existing approaches can take input such as updates to pose information for one or more joints and use this to generate animation. Such approaches, however, do not always provide for realistic animation, as the animation may not be smooth or may not represent how an actual character performing such an action would move over time. In order to improve a perceptive quality of such animation, approaches in accordance with various embodiments can utilize a trained neural network that takes as input a current state of a virtual character to be animated, for example, where that state includes information for the poses of different joints in two or three dimensions, as well as angles and other information that may be useful for the animation. This network can then predict how this character would appear in one or more subsequent frames of animation, or subsequent character states. Such a process can be performed recursively to generate the data for these frames. Approaches in accordance with various embodiments can provide improved results by starting with a single initial frame, but then using this to predict a sequence of subsequent frames, where each frame is predicted from a result of a previous frame. This generated sequence can then be compared with a ground truth sequence during training, such as by using a generative adversarial network (GAN). Differences between the ground truth and generated animation sequences can be minimized, whereby a specific objective function does not need to be manually defined. Minimizing the differences between the generated and ground truth animation sequences during training can also help to improve the quality of network predictions for single frames at inference time. 
     In at least one embodiment, content to be presented may include various types of content, as may include virtual reality (VR), augmented reality (AR), gaming, audio, or video content. In at least one embodiment, this content can be presented using a client device  202  as illustrated in system architecture  200  of  FIG. 2 . Client device  202  may include any appropriate device capable of at least presenting such content, as may include a desktop computer, notebook computer, set-top box, streaming device, gaming console, smart phone, tablet computer, VR headset, AR goggles, a wearable computer, or a smart television. In at least one embodiment, this content may include content transmitted across at least one network  212  from a content server  220  to a client device  202 . In at least one embodiment, a content presentation application  224  executing on content server  220  can initiate a session associated with client device  202 , using a session manager  226  and user data stored in a user database  234 , and can cause content  232  to be rendered using a rendering engine  228 , if needed for this type of content, and transmitted to client device  202  using an appropriate stream manager  222 . In at least one embodiment, client device  202  receiving this content can provide this content to a content presentation application  204 , which may also include a rendering engine  206 , for presentation via client device  202  (such as video content through a display  208 ), and audio (such as sounds and music), through at least one audio playback device  210 , such as speakers or headphones. In at least one embodiment, this content may already be stored on, or accessible to, client device  202  such that transmission over network  212  is not required. In at least one embodiment, a transmission mechanism other than streaming can also be used to transfer this content from server  220 , or content database  232 , to client device  202 . 
     In at least one embodiment where client device  202  communicates with a remote server, application  224  can include a content manager  230  that can analyze content before this content is transmitted to client device  202 . Content manager  230  can include one or more neural networks that are able to analyze this content after at least a few initial bytes are rendered and transmitted for client device  202 . In at least one embodiment, content manager  230  will utilize these neural networks, or other deep learning mechanisms, to predict motion for one or more subsequent frames to be used in generating more realistic animation. In at least one embodiment, content manager  230  can store text or other data for generated animation to a content database  232 . In various embodiments, this animation can be rendered using a rendering engine  228  on server  220  or a rendering engine  206  on client device  202 . In at least one embodiment, this content can be transmitted to client device  202  for display or other presentation. In other embodiments, tasks such as inferencing and rendering can be performed by application  204  executing on client device  202 , among other such options. 
     In at least one embodiment a controller can be provided for virtual characters capable of adapting to different environments and generating realistic animation. Certain existing approaches utilize networks that are able to predict motion of a virtual character based on its pose in the previous frame, as well as user input commands. While there has been a lot of progress in developing such network architecture, little attention has been focused on improving training procedures and objective functions used for these and other such networks. Accordingly, approaches in accordance with various embodiments can provide a training approach that is applicable to various architectures, including any motion prediction architecture, and provides for improvement in quality and variability of the generated animation, which can make this generated animation more visually appealing. In various embodiments, a recurrent generative adversarial network (GAN) can be utilized, which avoids the need for a hand-crafted objective function as in prior solutions. A recurrent GAN can help generated animation closely match corresponding motion data, such as motion capture data. 
     As mentioned, creating realistic animation of virtual characters is a challenging problem, as it can involve developing a controller that is fast and capable of adapting the motion of the character to different obstacles and terrain changes. Even with the availability of motion capture datasets, designing such a controller is a complicated problem, as it needs to incorporate information from large datasets of examples, have a low memory footprint, and be very fast at runtime. Approaches in accordance with various embodiments utilize deep learning techniques to help provide such a controller. 
     Certain controllers may utilize networks such as convolutional or autoregressive networks. Convolutional approaches can perform convolutional operation in the temporal domain, which leads to transformation of the input signal into desired output. While effective for various offline applications, these types of approaches are generally not suitable for real-time applications, such as for computer gaming, due at least to the fact that they typically require a full sequence of frames available as input to the network. Such a requirement presents a severe limitation, as the future frames may be affected by the actions of the user and, therefore, cannot be known in advance. Some approaches may rely on autoregressive models, such as restricted Boltzmann machines and recurrent neural networks (RNNs). The former ones typically require a model-complex training procedure, while the parameters of the latter ones can be learned with a simple stochastic gradient descent. Overall, these methods are more suitable for online applications, for example, as these methods do not require the knowledge about the future frames. A significant limitation of these techniques, however, lies in the fact that due to their autoregressive nature they tend to die out or explode when the error from multiple consecutive predictions is accumulated. 
     In various embodiments, a training procedure can be utilized that helps to increase a perceptual quality of generated animation. In at least one embodiment, this involves use of an autoregressive training procedure that enables a network to correct for its own pose estimation errors, without requiring any manual parameter tuning. Such a training procedure can utilize a generic objective function that is based on a conditional generative adversarial neural network architecture, which reduces discrepancy between distributions of generated character pose sequences and those from the motion capture data. Such an approach can be general in nature, such that it can be utilized with virtually any motion prediction architecture. 
     In at least one embodiment, input and output for a motion prediction network can utilize respective data formats. In at least one embodiment, inputs to a motion prediction network can be represented as the following vector at frame t: 
       x t ={t t   p ,t t   d ,t t   a ,j t−1   p ,j t−1   v ,h t } 
     where the description of the components is defined in Table 1. Output from this motion prediction network is vector y t , which can have the following form: 
       y t ={t t+1   p ,t t+1   d ,j t   p ,j t   v ,j t   r ,{dot over (r)} t   x ,{dot over (r)} t   z ,{dot over (r)} t   a ,f t   c ,f t   h } 
     with notations also as defined in Table 1. 
     
       
         
           
               
             
               
                 TABLE 1 
               
               
                   
               
               
                 Example notations that are used to describe the input and the 
               
               
                 output states of the motion prediction system: 
               
               
                   
               
             
            
               
                   
               
            
           
           
               
               
               
            
               
                 Trajectory 
                 t t   p  ∈     2t   
                 Positions of t trajectory samples 
               
               
                   
                 t t   d  ∈     2t   
                 Forward facing directions along the trajectory 
               
               
                   
                 t t   a  ∈     2t   
                 One-hot encoding of action types (gaits) along 
               
               
                   
                   
                 the trajectory 
               
               
                 Joints 
                 j t   p  ∈     3J   
                 3D joint positions relative to the root position 
               
               
                   
                 j t   v  ∈     3J   
                 Relative joint velocities 
               
               
                   
                 j t   r  ∈     6J   
                 Joint rotations 
               
               
                   
                 J 
                 Number of joints 
               
               
                   
                 {dot over (r)} t   [x|z]  ∈    
                 Root translational x and z velocities 
               
               
                   
                 {dot over (r)} t   a  ∈    
                 Root angular velocity in the horizontal plane 
               
               
                   
                 f t   c  ∈     4   
                 Probability of foot contacts 
               
               
                   
                 f t   h  ∈     4   
                 Foot height 
               
               
                 Other 
                 h t  ∈     36   
                 Height maps for the current time step i 
               
               
                   
                 p t  ∈    
                 Motion phase at the time step i 
               
               
                   
               
            
           
         
       
     
     Additionally to the aforementioned input and output vectors, a Phase-Functioned Neural Network (PFNN)-based approach receives a single real-valued parameter p t , which denotes the phase of the current motion cycle at timestep t. Output of the PFNN can be augmented with an additional predicted parameter Δp t  which corresponds to the phase increment that can be used to estimate the value of p t+1  during the autoregressive inference. 
     In various embodiments, architectures of two different motion prediction networks can be utilized as backbone architecture for illustration. In at least one embodiment, a Phase-Functioned Neural Network (PFNN) can be utilized that consists of at least two parts: neural network F and phase function ⊖, which defines the parameters of F. The neural network F can be a three-layer network, which receives as input vector x t  as defined elsewhere herein. Further, F=F(x t ;α) can depend on a set of parameters α, namely the weights and the biases of the fully-connected layers. These parameters may not be allowed to change freely, instead they can be computed as: 
       α=⊖(p,β)
 
     where β={α i } i=0   k−1  are the trainable sets of parameters, k is the number of control points and ⊖ is the phase function that is taken to be the cubic Catmull-Rom spline. The latter has several important benefits, as ⊖ can be easily made cyclic by forcing the start and end control points to be the same. Further, ⊖ can vary smoothly with the change of the phase parameter. In at least one embodiment, a number of control points k can be set to a value such as 4. For this case, phase function ⊖ can have the following look: 
     
       
         
           
             
               ⊖ 
               
                 ( 
                 
                   p 
                   , 
                   β 
                 
                 ) 
               
             
             = 
             
               
                 
                   
                     1 
                     2 
                   
                   ⁡ 
                   
                     [ 
                     
                       
                         
                           
                             2 
                             ⁢ 
                             
                               α 
                               
                                 k 
                                 1 
                               
                             
                           
                         
                       
                       
                         
                           
                             
                               α 
                               
                                 k 
                                 2 
                               
                             
                             - 
                             
                               α 
                               
                                 k 
                                 0 
                               
                             
                           
                         
                       
                       
                         
                           
                             
                               2 
                               ⁢ 
                               
                                 α 
                                 
                                   k 
                                   0 
                                 
                               
                             
                             - 
                             
                               5 
                               ⁢ 
                               
                                 α 
                                 
                                   k 
                                   1 
                                 
                               
                             
                             + 
                             
                               4 
                               ⁢ 
                               
                                 α 
                                 
                                   k 
                                   2 
                                 
                               
                             
                             - 
                             
                               α 
                               
                                 k 
                                 3 
                               
                             
                           
                         
                       
                       
                         
                           
                             
                               3 
                               ⁢ 
                               
                                 α 
                                 
                                   k 
                                   1 
                                 
                               
                             
                             - 
                             
                               3 
                               ⁢ 
                               
                                 α 
                                 
                                   k 
                                   2 
                                 
                               
                             
                             + 
                             
                               α 
                               
                                 k 
                                 3 
                               
                             
                             - 
                             
                               α 
                               
                                 k 
                                 0 
                               
                             
                           
                         
                       
                     
                     ] 
                   
                 
                 T 
               
               ⁡ 
               
                 [ 
                 
                   
                     
                       1 
                     
                   
                   
                     
                       v 
                     
                   
                   
                     
                       
                         v 
                         2 
                       
                     
                   
                   
                     
                       
                         v 
                         3 
                       
                     
                   
                 
                 ] 
               
             
           
         
       
     
     where β={a i }, i=[0 . . . 3] and the parameters v and {k n }, n=[0 . . . 3] are defined as follows: 
     
       
         
           
             
               v 
               = 
               
                 
                   
                     4 
                     ⁢ 
                     p 
                   
                   
                     2 
                     ⁢ 
                     π 
                   
                 
                 ⁢ 
                 
                   ( 
                   
                     mod 
                     ⁢ 
                     
                         
                     
                     ⁢ 
                     1 
                   
                   ) 
                 
               
             
             ; 
             
               
                 k 
                 n 
               
               = 
               
                 
                   ⌊ 
                   
                     
                       4 
                       ⁢ 
                       p 
                     
                     
                       2 
                       ⁢ 
                       π 
                     
                   
                   ⌋ 
                 
                 + 
                 n 
                 - 
                 
                   1 
                   ⁢ 
                   
                     ( 
                     
                       mod 
                       ⁢ 
                       
                           
                       
                       ⁢ 
                       4 
                     
                     ) 
                   
                 
               
             
           
         
       
     
     In at least one embodiment, architecture of a Mode-Adaptive Neural Network (MANN) is greatly inspired by that of a PFNN network, but with some important differences. For example, instead of using a phase function to compute the parameters α of the motion prediction network F(x t ;α) a gaiting network Ω({circumflex over (x)} t ) could be utilized. The latter operates on the input vector {circumflex over (x)} t , which is a subset of x t  that includes the foot end effector velocities, current action variables and the desired velocity of the character. Given this input Ω({circumflex over (x)} t ) then predicts a set of weights w={w i }, i=[0 . . . k−1] that are used to compute the parameters a of the motion prediction network F as follows: 
     
       
         
           
             
               α 
               = 
               
                 
                   ∑ 
                   
                     i 
                     = 
                     0 
                   
                   
                     k 
                     - 
                     1 
                   
                 
                 ⁢ 
                 
                   
                     w 
                     i 
                   
                   ⁢ 
                   
                     α 
                     i 
                   
                 
               
             
             , 
             
               w 
               = 
               
                 
                   
                     { 
                     
                       w 
                       i 
                     
                     } 
                   
                   
                     i 
                     = 
                     0 
                   
                   
                     k 
                     - 
                     1 
                   
                 
                 = 
                 
                   
                     
                       Ω 
                       ⁡ 
                       
                         ( 
                         
                           
                             x 
                             ¯ 
                           
                           t 
                         
                         ) 
                       
                     
                     : 
                     
                       
                         ∑ 
                         
                           i 
                           = 
                           0 
                         
                         
                           k 
                           - 
                           1 
                         
                       
                       ⁢ 
                       
                         w 
                         i 
                       
                     
                   
                   = 
                   1 
                 
               
             
           
         
       
     
     where 
     
       
         
           
             
               { 
               
                 α 
                 i 
               
               } 
             
             ⁢ 
             
               
                 k 
                 - 
                 1 
               
               
                 i 
                 = 
                 0 
               
             
           
         
       
     
     are the sets of trainable parameters similar to the ones defined in paragraph 0028. An advantage of such an approach resides in its applicability to a broader variety of motions as compared to PFNN, and does not require a tedious procedure of labeling the value of a phase parameter during preparation of a training dataset. 
     While both MANN- and PFNN-based methods have shown great improvement in animation quality with respect to conventional deep neural networks, approaches in accordance with various embodiments can provide for further improvement in generated animation, such that this animation is more realistic from a human viewer perspective. 
     Certain motion prediction methods rely on an autoregressive inference strategy, whereby at each time step the prediction of the network is used as the input to the network at the subsequent step. Such an approach, however, makes the inference task very hard for the network as at training time is has only seen the ground truth poses as input. Consequently this may lead to a decrease in animation quality. 
     Approaches in accordance with various embodiments can mitigate this issue by running a motion prediction network in an autoregressive manner at training time as well. In at least one embodiment, a simulator could be included in a training loop, but such an approach may be quite costly and lead to significant decrease in the training speed. Instead, approaches in accordance with various embodiments can start with a sequence of inputs {x t },t∈[1 . . . T] and the respective sequence of ground-truth labels {y t }. Output z 1  of the motion prediction network F can then be computed for the first element of input sequence x 1 . In order to compute the prediction of F for the second element of this input sequence (x 2 ) in an autoregressive manner, pose P 1  of the character from the prediction z 1  would need to be fed back to F together with the updated values for other parameters of the state R 1  (e.g., height maps) computed from the simulator. The latter may however be relatively slow, such that these values may be extracted instead from the second element of input sequence x 2 . This operation can then be repeated for all the elements of the training sequence, leaving an approximation of an autoregressive training process. Such an approach is briefly summarized in the following algorithm: 
     
       
         
           
               
             
               
                   
               
               
                 Algorithm 1: Autoregressive training of the motion prediction network 
               
               
                   
               
             
            
               
                   
               
            
           
           
               
            
               
                 Data: Input: {x t } : x t , t ∈ [1..T], 
               
               
                 Motion prediction network: F(·) 
               
               
                 Result: Prediction {z t } : z t , t ∈ [1..T] 
               
               
                 for t in [1..T] do 
               
               
                  | if t = 1 then 
               
            
           
           
               
               
            
               
                  | | z 0  ← F(x 0 ) ; 
                   // Estimate the prediction for the first time step 
               
               
                  | else 
                   
               
               
                  | | {P t , R t } ← Split(x t ) ; 
                  // Split predicted data to pose P t  and all the rest R t   
               
               
                  | | {{circumflex over (P)} t−1 , {circumflex over (R)} t−1 } ← 
                 // Split predicted data to pose {circumflex over (P)} t−1  and all the rest {circumflex over (R)} t−1   
               
               
                  | | Split(z t−1 ) ; 
                   
               
               
                  | | {circumflex over (X)} t  ← 
                 // Form current input {circumflex over (X)} t  from predicted pose {circumflex over (P)} t−1  and ground- 
               
               
                  | | Combine({circumflex over (P)} t−1 , R t ) ; 
                                       truth R t   
               
               
                  | | z t  ← F({circumflex over (X)} t ) ; 
                    // Estimate motion prediction for the current time step 
               
               
                  | end 
                   
               
               
                 end 
               
               
                   
               
            
           
         
       
     
     Though such an approach may be an approximation of autoregressive training, it can achieve various benefits, such as the network seeing imperfect poses of a character, which leads to better generalization and ability to correct for the pose estimation mistakes. Further, the training does not include the possibly time-consuming simulation step, which allows to preserve the training speed. 
     Given the estimated autoregressive predictions, the average Mean Squared Error (MSE) can be computed across all the time steps [1 . . . T], as may be given by: 
     
       
         
           
             L 
             = 
             
               
                 1 
                 T 
               
               ⁢ 
               
                 
                   ∑ 
                   
                     t 
                     = 
                     1 
                   
                   T 
                 
                 ⁢ 
                 
                   ( 
                   
                     
                        
                       
                         
                           z 
                           t 
                         
                         - 
                         
                           y 
                           t 
                         
                       
                        
                     
                     
                       L 
                       2 
                     
                   
                   ) 
                 
               
             
           
         
       
     
     where z t ,t∈[1 . . . T] is the network predictions computed as described in Algorithm 1. 
     Performance of a PFNN-based approach can be quite stable even when the training dataset is very unbalanced in terms of the number of samples with, for example, flat and rocky terrains. This stability comes, at least in part, from the fact that PFNN uses a phase of the current motion gait, which serves as a strong prior. While being more flexible than PFNN, a MANN-based method does not rely on the phase of the motion gait at training and inference times, and therefore depends on the quality of the training datasets and may easily overfit to when the data for a specific type of motion or environment is scarce. This may lead to some artifacts like leg sliding or unrealistically looking animation. 
     Various embodiments can address this dataset imbalance problem by re-weighting the contribution of each of the training samples to the final objective function based, at least in part, on a frequency of appearance the respective height map type in the dataset. Such an approach can involve clustering the data in the training set by corresponding height maps using a k-means clustering, for example, with a fixed number of clusters K. K is a parameter of the algorithm presented previously. For each of the clusters C k , k=[1 . . . K] a number of elements can be computed from the dataset N C     k    that belong to it, which provides statistics of appearance of different types of environments in the training data. A majority of the data belongs to cluster 0, which essentially corresponds to flat surfaces. After clustering has completed, a contribution of each of the elements in the dataset to the final objective function can be computed as follows: 
     
       
         
           
             
               
                 
                   
                     w 
                     i 
                   
                   = 
                   
                     
                       off 
                       ( 
                       
                         
                           
                             N 
                             
                               C 
                               ⁡ 
                               
                                 ( 
                                 i 
                                 ) 
                               
                             
                           
                           
                             
                               ∑ 
                               
                                 k 
                                 = 
                                 1 
                               
                               K 
                             
                             ⁢ 
                             
                               N 
                               
                                 C 
                                 k 
                               
                             
                           
                         
                         + 
                         off 
                       
                       ) 
                     
                     
                       - 
                       1 
                     
                   
                 
               
               
                 
                     
                 
               
             
           
         
       
     
     Here w i  is the contribution of the i th  training sample inside the objective function; C(i) is the cluster that the i th  training sample belongs to; N C  is the coordinality of the data cluster C and off=0.25 is a positive offset. 
     In order to make the generated animation even closer to the ground truth animation, an adversarial objective term can be added to the optimization loss. To compute this adversarial objective, a temporal discriminator D can be used that receives as input sequences of character poses, both predicted by the network and extracted from the ground truth data. A goal of D in this situation is then to distinguish whether each of these sequences is real or not. The temporal nature of D allows it to learn the smooth transitions between gaits and forces the motion prediction network to better adjust to high frequency information of the training data which, in turn, leads to higher variability of predicted motion. 
     In order to efficiently gather information from the predictions of a prediction network for multiple consecutive time steps, one or more Long Short-Term Memory (LSTM) units can be utilized in the discriminator architecture. In at least one embodiment, this model receives a sequence of full output states from the motion prediction network and returns the latent representation that gather the information from all the temporal predictions. This latent representation is then processed by a sequence of fully-connected layers that ultimately predict whether the sequence of input samples is generated or coming from the ground-truth data. 
     Further, a discriminator model in at least one embodiment can be conditioned on the true height map estimates and gait values that correspond to the current time step. This allows the discriminator to better focus on different types of motions for different types of environments and gaits. 
     In at least one embodiment, a prediction model can be trained using a Wassertein objective function. Parameters co of the discriminator D ω (⋅) can be trained by minimizing an objective such as may be given by: 
     
       
         
           
             
               
                 
                   ω 
                   ← 
                   
                     
                       
                         ∇ 
                         ω 
                       
                       ⁢ 
                       
                         1 
                         m 
                       
                     
                     ⁢ 
                     
                       ( 
                       
                         
                           
                             ∑ 
                             
                               i 
                               = 
                               1 
                             
                             B 
                           
                           ⁢ 
                           
                             
                               D 
                               ω 
                             
                             ⁡ 
                             
                               ( 
                               
                                 y 
                                 i 
                               
                               ) 
                             
                           
                         
                         - 
                         
                           
                             ∑ 
                             
                               i 
                               = 
                               1 
                             
                             B 
                           
                           ⁢ 
                           
                             
                               D 
                               ω 
                             
                             ⁡ 
                             
                               ( 
                               
                                 
                                   F 
                                   θ 
                                 
                                 ⁡ 
                                 
                                   ( 
                                   
                                     x 
                                     i 
                                   
                                   ) 
                                 
                               
                               ) 
                             
                           
                         
                       
                       ) 
                     
                   
                 
               
               
                 
                     
                 
               
             
           
         
       
     
     where θ and ω are parameters of the motion prediction network and discriminator respectively, and B denotes the number of samples in the batch. Motion prediction network F θ (⋅) can then be trained using an objective term, such as: 
     
       
         
           
             
               
                 
                   θ 
                   ← 
                   
                     
                       - 
                       
                         
                           ∇ 
                           θ 
                         
                         ⁢ 
                         
                           1 
                           m 
                         
                       
                     
                     ⁢ 
                     
                       
                         ∑ 
                         
                           i 
                           = 
                           1 
                         
                         B 
                       
                       ⁢ 
                       
                         
                           D 
                           ω 
                         
                         ⁡ 
                         
                           ( 
                           
                             
                               F 
                               θ 
                             
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     which is added to the main loss function. Such a loss function can be more effective, compared to a commonly-used Cross-Entropy loss in bridging the gap between the generated and ground truth distributions of character poses. 
     In at least one embodiment, adding adversarial loss to the optimization objective can significantly improve the visual quality of the generated animation using the PFNN motion prediction network. However, the quality of the MANN method very much depends on the size of the dataset that is being used for training. As such, if the dataset is not large enough then a MANN-based approach may be is prone to overfitting to the training samples, which can lead to the appearance of various artifacts at inference time, such as leg sliding and unrealistic pose generation. To overcome this problem, approaches in accordance with various embodiments add phase-based regularization to the network for such gaits, where the phase is available. This essentially makes the model more robust to different environments, such that the resulting animation looks more realistic. To achieve this and preserve the independence of the MANN architecture from phase at the inference time, ground-truth phase data can be added as an additional input to the discriminator network. Conditioning the discriminator in such a way can provide a necessary temporal regularization to the motion prediction network and result in generated animation of higher quality. 
       FIG. 3  illustrates an example architecture  300  that can be utilized in accordance with various embodiments. In this example, the input state data  304  corresponds to a sequence of consecutive frames X={x t },t∈[1 . . . T], which are passed through a motion prediction network  312 . The input state data  304  can be passed through the motion prediction network  312  in an autoregressive manner, as depicted by Algorithm 1 presented previously. The motion prediction network  312  can produce predicted state data  314  that includes a sequence of predicted states Y={y t }. This sequence of predicted states can then be passed to a temporal discriminator  318  of a generative adversarial network (GAN) synthesized on top of the motion prediction network  312 . Input to the discriminator  318  can also include separated input gait data  306  (e.g., climbing, running, or jumping) and height maps  308 , which may have been concatenated with the predicted state data using a concatenator  316 . In at least one embodiment, a temporal discriminator network  318  can include at least one long short-term memory (LSTM) module, followed by a sequence of fully-connected layers (FC). In at least one embodiment, these can be separated by a leaky ReLU activation function. The temporal discriminator network can be conditioned on the gait of the current motion {t t   a } and on the heightmap of the environment that surrounds the virtual character {h t } The discriminator  318  can then output an adversarial loss  322  determined as discussed elsewhere herein. The predicted state data  314  from the motion prediction network  312  can also be compared against the ground truth state data  302  to determine another loss value, such as a Mean Squared Error (MSE) loss. These loss values can then be used to adjust network parameters for a subsequent training pass in order to attempt to optimize the network and minimize an overall loss determined by a loss function that includes these two loss values. 
     In an experiment using such an architecture in accordance with one embodiment, an Adam optimizer was utilized with learning rate 10 −4 . A sequence of T=9 frames was used as input to an autoregressive training procedure as discussed herein. A value such as this has been determined to be enough to generate a strong temporal context for the discriminator in many embodiments, which in turn allows generating both realistic and variable motions. In various experiments a motion capture dataset is utilized with preprocessing performed to convert the raw data to a set of pairs that contain the input to the motion prediction network and the ground truth in the format described elsewhere herein.  FIG. 4A  illustrates a comparison  400  of results for different GAN-based approaches on this dataset (no GAN, cross-entropy based GAN, least squares GAN, and Wasserstein GAN). A GAN in accordance with at least one embodiment can utilize an objective function based on a Wasserstein metric, which provides a distance function defined between probability distributions on a given metric space. Percentages next to the bars show the relative improvement with respect to the method without GAN, according to the validation set. As illustrated, the quality of the generated animation can depend significantly on the choice of the GAN optimization objective. In all the cases a PFNN was utilized as the backbone motion prediction network. The architecture of the discriminator was also fixed, with changes only to the way in which the GAN objective function is computed. The performance of these techniques on the validation data was evaluated by computing the Mean Squared Error (MSE) of the predicted pose of the character with respect to the ground truth data, as illustrated in  FIG. 4A . Other methods fail to improve the performance, which happens due to the fact that the distributions of the predicted and ground truth character poses are quite different from each other and the GAN objective function is not able to successfully bridge this gap.  FIG. 4B  provides a graph  450  illustrating an evaluation of visual performance of different methods according to the Mean Opinion Score (MOS) measure. 
     The performance of an approach in accordance with at least one embodiment can be compared to that of the baseline methods of PFNN and MANN. Such an approach can modify the optimization objective and the training strategy, while keeping the motion prediction architecture untouched. Other techniques could be utilized as well, as may be capable of generating character motion, thus making this approach substantially general. Such an approach can improve the perceived quality of generated animation for a motion prediction network by modifying its objective function and training procedure. 
       FIG. 5  illustrates an example process  500  for training a motion prediction model that can be utilized in accordance with various embodiments. It should be understood for this and other processes discussed herein that there can be additional, alternative, or fewer steps performed in similar or at least partially alternative orders, or in parallel, within the scope of the various embodiments unless otherwise stated. In this example, input state data is received  502  for a sequence of frames of training data. For training purposes, associated ground truth data can also be received or obtained in this training data set. This first input state data can be provided  504  as input to a motion prediction network, and a prediction received for the next frame. In at least some embodiments the state data may undergo some amount of preprocessing, at least to place the data in an appropriate format for the motion prediction network. This prediction can be aggregated  506  with the gait and heat map values from the input state data and passed to the motion prediction network. This aggregating can be repeated for all frames in the input sequence, and a predicted sequence frames of animation passed  508  as input to a temporal discriminator. Using the gait and map data as conditions, the temporal discriminator of a generative adversarial network (GAN), synthesized on top of the motion prediction network, can determine  510  an adversarial loss value. In other embodiments, a temporal convolutional or recurrent network could be used as well. A loss such as a Mean Squared Error (MSE) loss value can also be determined  512 , in sequence or in parallel, by comparing the predicted and ground truth frames. One or more parameters of this motion prediction network can then be adjusted  514  to minimize an overall loss, using a loss function that includes terms for both the MSE loss and the adversarial loss. A determination can be made  516  as to whether an end condition or criterion has been satisfied, such as where a maximum number of training passes has been reached or a convergence criterion has been satisfied. In at least one embodiment, if an end condition has been satisfied then these trained models can be provided  518  for inferencing, otherwise training can continue with a next pass or iteration. 
     In at least one embodiment, a process  600  illustrated in  FIG. 6  can be used, at inference time, to infer notion or animation data. In at least one embodiment, one or more instances of frame data can be received  602  and provided  604  as input to a trained model, such as a trained motion prediction model. In at least one embodiment, inferred data can be received  456  as output from this trained model, where that inferred data may relate to predicted motion, state, or animation data for at least one instance of frame data. 
     Inference and Training Logic 
       FIG. 7 a    illustrates inference and/or training logic  715  used to perform inferencing and/or training operations associated with one or more embodiments. Details regarding inference and/or training logic  715  are provided below in conjunction with  FIGS. 7A and/or 7B . 
     In at least one embodiment, inference and/or training logic  715  may include, without limitation, code and/or data storage  701  to store forward and/or output weight and/or input/output data, and/or other parameters to configure neurons or layers of a neural network trained and/or used for inferencing in aspects of one or more embodiments. In at least one embodiment, training logic  715  may include, or be coupled to code and/or data storage  701  to store graph code or other software to control timing and/or order, in which weight and/or other parameter information is to be loaded to configure, logic, including integer and/or floating point units (collectively, arithmetic logic units (ALUs). In at least one embodiment, code, such as graph code, loads weight or other parameter information into processor ALUs based on an architecture of a neural network to which the code corresponds. In at least one embodiment, code and/or data storage  701  stores weight parameters and/or input/output data of each layer of a neural network trained or used in conjunction with one or more embodiments during forward propagation of input/output data and/or weight parameters during training and/or inferencing using aspects of one or more embodiments. In at least one embodiment, any portion of code and/or data storage  701  may be included with other on-chip or off-chip data storage, including a processor&#39;s L1, L2, or L3 cache or system memory. 
     In at least one embodiment, any portion of code and/or data storage  701  may be internal or external to one or more processors or other hardware logic devices or circuits. In at least one embodiment, code and/or code and/or data storage  701  may be cache memory, dynamic randomly addressable memory (“DRAM”), static randomly addressable memory (“SRAM”), non-volatile memory (e.g., Flash memory), or other storage. In at least one embodiment, choice of whether code and/or code and/or data storage  701  is internal or external to a processor, for example, or comprised of DRAM, SRAM, Flash or some other storage type may depend on available storage on-chip versus off-chip, latency requirements of training and/or inferencing functions being performed, batch size of data used in inferencing and/or training of a neural network, or some combination of these factors. 
     In at least one embodiment, inference and/or training logic  715  may include, without limitation, a code and/or data storage  705  to store backward and/or output weight and/or input/output data corresponding to neurons or layers of a neural network trained and/or used for inferencing in aspects of one or more embodiments. In at least one embodiment, code and/or data storage  705  stores weight parameters and/or input/output data of each layer of a neural network trained or used in conjunction with one or more embodiments during backward propagation of input/output data and/or weight parameters during training and/or inferencing using aspects of one or more embodiments. In at least one embodiment, training logic  715  may include, or be coupled to code and/or data storage  705  to store graph code or other software to control timing and/or order, in which weight and/or other parameter information is to be loaded to configure, logic, including integer and/or floating point units (collectively, arithmetic logic units (ALUs). In at least one embodiment, code, such as graph code, loads weight or other parameter information into processor ALUs based on an architecture of a neural network to which the code corresponds. In at least one embodiment, any portion of code and/or data storage  705  may be included with other on-chip or off-chip data storage, including a processor&#39;s L1, L2, or L3 cache or system memory. In at least one embodiment, any portion of code and/or data storage  705  may be internal or external to on one or more processors or other hardware logic devices or circuits. In at least one embodiment, code and/or data storage  705  may be cache memory, DRAM, SRAM, non-volatile memory (e.g., Flash memory), or other storage. In at least one embodiment, choice of whether code and/or data storage  705  is internal or external to a processor, for example, or comprised of DRAM, SRAM, Flash or some other storage type may depend on available storage on-chip versus off-chip, latency requirements of training and/or inferencing functions being performed, batch size of data used in inferencing and/or training of a neural network, or some combination of these factors. 
     In at least one embodiment, code and/or data storage  701  and code and/or data storage  705  may be separate storage structures. In at least one embodiment, code and/or data storage  701  and code and/or data storage  705  may be same storage structure. In at least one embodiment, code and/or data storage  701  and code and/or data storage  705  may be partially same storage structure and partially separate storage structures. In at least one embodiment, any portion of code and/or data storage  701  and code and/or data storage  705  may be included with other on-chip or off-chip data storage, including a processor&#39;s L1, L2, or L3 cache or system memory. 
     In at least one embodiment, inference and/or training logic  715  may include, without limitation, one or more arithmetic logic unit(s) (“ALU(s)”)  710 , including integer and/or floating point units, to perform logical and/or mathematical operations based, at least in part on, or indicated by, training and/or inference code (e.g., graph code), a result of which may produce activations (e.g., output values from layers or neurons within a neural network) stored in an activation storage  720  that are functions of input/output and/or weight parameter data stored in code and/or data storage  701  and/or code and/or data storage  705 . In at least one embodiment, activations stored in activation storage  720  are generated according to linear algebraic and or matrix-based mathematics performed by ALU(s)  710  in response to performing instructions or other code, wherein weight values stored in code and/or data storage  705  and/or code and/or data storage  701  are used as operands along with other values, such as bias values, gradient information, momentum values, or other parameters or hyperparameters, any or all of which may be stored in code and/or data storage  705  or code and/or data storage  701  or another storage on or off-chip. 
     In at least one embodiment, ALU(s)  710  are included within one or more processors or other hardware logic devices or circuits, whereas in another embodiment, ALU(s)  710  may be external to a processor or other hardware logic device or circuit that uses them (e.g., a co-processor). In at least one embodiment, ALUs  710  may be included within a processor&#39;s execution units or otherwise within a bank of ALUs accessible by a processor&#39;s execution units either within same processor or distributed between different processors of different types (e.g., central processing units, graphics processing units, fixed function units, etc.). In at least one embodiment, code and/or data storage  701 , code and/or data storage  705 , and activation storage  720  may be on same processor or other hardware logic device or circuit, whereas in another embodiment, they may be in different processors or other hardware logic devices or circuits, or some combination of same and different processors or other hardware logic devices or circuits. In at least one embodiment, any portion of activation storage  720  may be included with other on-chip or off-chip data storage, including a processor&#39;s L1, L2, or L3 cache or system memory. Furthermore, inferencing and/or training code may be stored with other code accessible to a processor or other hardware logic or circuit and fetched and/or processed using a processor&#39;s fetch, decode, scheduling, execution, retirement and/or other logical circuits. 
     In at least one embodiment, activation storage  720  may be cache memory, DRAM, SRAM, non-volatile memory (e.g., Flash memory), or other storage. In at least one embodiment, activation storage  720  may be completely or partially within or external to one or more processors or other logical circuits. In at least one embodiment, choice of whether activation storage  720  is internal or external to a processor, for example, or comprised of DRAM, SRAM, Flash or some other storage type may depend on available storage on-chip versus off-chip, latency requirements of training and/or inferencing functions being performed, batch size of data used in inferencing and/or training of a neural network, or some combination of these factors. In at least one embodiment, inference and/or training logic  715  illustrated in  FIG. 7 a    may be used in conjunction with an application-specific integrated circuit (“ASIC”), such as Tensorflow® Processing Unit from Google, an inference processing unit (IPU) from Graphcore™, or a Nervana® (e.g., “Lake Crest”) processor from Intel Corp. In at least one embodiment, inference and/or training logic  715  illustrated in  FIG. 7 a    may be used in conjunction with central processing unit (“CPU”) hardware, graphics processing unit (“GPU”) hardware or other hardware, such as field programmable gate arrays (“FPGAs”). 
       FIG. 7 b    illustrates inference and/or training logic  715 , according to at least one or more embodiments. In at least one embodiment, inference and/or training logic  715  may include, without limitation, hardware logic in which computational resources are dedicated or otherwise exclusively used in conjunction with weight values or other information corresponding to one or more layers of neurons within a neural network. In at least one embodiment, inference and/or training logic  715  illustrated in  FIG. 7 b    may be used in conjunction with an application-specific integrated circuit (ASIC), such as Tensorflow® Processing Unit from Google, an inference processing unit (IPU) from Graphcore™, or a Nervana® (e.g., “Lake Crest”) processor from Intel Corp. In at least one embodiment, inference and/or training logic  715  illustrated in  FIG. 7 b    may be used in conjunction with central processing unit (CPU) hardware, graphics processing unit (GPU) hardware or other hardware, such as field programmable gate arrays (FPGAs). In at least one embodiment, inference and/or training logic  715  includes, without limitation, code and/or data storage  701  and code and/or data storage  705 , which may be used to store code (e.g., graph code), weight values and/or other information, including bias values, gradient information, momentum values, and/or other parameter or hyperparameter information. In at least one embodiment illustrated in  FIG. 7 b   , each of code and/or data storage  701  and code and/or data storage  705  is associated with a dedicated computational resource, such as computational hardware  702  and computational hardware  706 , respectively. In at least one embodiment, each of computational hardware  702  and computational hardware  706  comprises one or more ALUs that perform mathematical functions, such as linear algebraic functions, only on information stored in code and/or data storage  701  and code and/or data storage  705 , respectively, result of which is stored in activation storage  720 . 
     In at least one embodiment, each of code and/or data storage  701  and  705  and corresponding computational hardware  702  and  706 , respectively, correspond to different layers of a neural network, such that resulting activation from one “storage/computational pair  701 / 702 ” of code and/or data storage  701  and computational hardware  702  is provided as an input to “storage/computational pair  705 / 706 ” of code and/or data storage  705  and computational hardware  706 , in order to mirror conceptual organization of a neural network. In at least one embodiment, each of storage/computational pairs  701 / 702  and  705 / 706  may correspond to more than one neural network layer. In at least one embodiment, additional storage/computation pairs (not shown) subsequent to or in parallel with storage computation pairs  701 / 702  and  705 / 706  may be included in inference and/or training logic  715 . 
     Data Center 
       FIG. 8  illustrates an example data center  800 , in which at least one embodiment may be used. In at least one embodiment, data center  800  includes a data center infrastructure layer  810 , a framework layer  820 , a software layer  830 , and an application layer  840 . 
     In at least one embodiment, as shown in  FIG. 8 , data center infrastructure layer  810  may include a resource orchestrator  812 , grouped computing resources  814 , and node computing resources (“node C.R.s”)  816 ( 1 )- 816 (N), where “N” represents any whole, positive integer. In at least one embodiment, node C.R.s  816 ( 1 )- 816 (N) may include, but are not limited to, any number of central processing units (“CPUs”) or other processors (including accelerators, field programmable gate arrays (FPGAs), graphics processors, etc.), memory devices (e.g., dynamic read-only memory), storage devices (e.g., solid state or disk drives), network input/output (“NW I/O”) devices, network switches, virtual machines (“VMs”), power modules, and cooling modules, etc. In at least one embodiment, one or more node C.R.s from among node C.R.s  816 ( 1 )- 816 (N) may be a server having one or more of above-mentioned computing resources. 
     In at least one embodiment, grouped computing resources  814  may include separate groupings of node C.R.s housed within one or more racks (not shown), or many racks housed in data centers at various geographical locations (also not shown). Separate groupings of node C.R.s within grouped computing resources  814  may include grouped compute, network, memory or storage resources that may be configured or allocated to support one or more workloads. In at least one embodiment, several node C.R.s including CPUs or processors may grouped within one or more racks to provide compute resources to support one or more workloads. In at least one embodiment, one or more racks may also include any number of power modules, cooling modules, and network switches, in any combination. 
     In at least one embodiment, resource orchestrator  812  may configure or otherwise control one or more node C.R.s  816 ( 1 )- 816 (N) and/or grouped computing resources  814 . In at least one embodiment, resource orchestrator  812  may include a software design infrastructure (“SDI”) management entity for data center  800 . In at least one embodiment, resource orchestrator may include hardware, software or some combination thereof. 
     In at least one embodiment, as shown in  FIG. 8 , framework layer  820  includes a job scheduler  822 , a configuration manager  824 , a resource manager  826  and a distributed file system  828 . In at least one embodiment, framework layer  820  may include a framework to support software  832  of software layer  830  and/or one or more application(s)  842  of application layer  840 . In at least one embodiment, software  832  or application(s)  842  may respectively include web-based service software or applications, such as those provided by Amazon Web Services, Google Cloud and Microsoft Azure. In at least one embodiment, framework layer  820  may be, but is not limited to, a type of free and open-source software web application framework such as Apache Spark™ (hereinafter “Spark”) that may utilize distributed file system  828  for large-scale data processing (e.g., “big data”). In at least one embodiment, job scheduler  822  may include a Spark driver to facilitate scheduling of workloads supported by various layers of data center  800 . In at least one embodiment, configuration manager  824  may be capable of configuring different layers such as software layer  830  and framework layer  820  including Spark and distributed file system  828  for supporting large-scale data processing. In at least one embodiment, resource manager  826  may be capable of managing clustered or grouped computing resources mapped to or allocated for support of distributed file system  828  and job scheduler  822 . In at least one embodiment, clustered or grouped computing resources may include grouped computing resource  814  at data center infrastructure layer  810 . In at least one embodiment, resource manager  826  may coordinate with resource orchestrator  812  to manage these mapped or allocated computing resources. 
     In at least one embodiment, software  832  included in software layer  830  may include software used by at least portions of node C.R.s  816 ( 1 )- 816 (N), grouped computing resources  814 , and/or distributed file system  828  of framework layer  820 . The one or more types of software may include, but are not limited to, Internet web page search software, e-mail virus scan software, database software, and streaming video content software. 
     In at least one embodiment, application(s)  842  included in application layer  840  may include one or more types of applications used by at least portions of node C.R.s  816 ( 1 )- 816 (N), grouped computing resources  814 , and/or distributed file system  828  of framework layer  820 . One or more types of applications may include, but are not limited to, any number of a genomics application, a cognitive compute, and a machine learning application, including training or inferencing software, machine learning framework software (e.g., PyTorch, TensorFlow, Caffe, etc.) or other machine learning applications used in conjunction with one or more embodiments. 
     In at least one embodiment, any of configuration manager  824 , resource manager  826 , and resource orchestrator  812  may implement any number and type of self-modifying actions based on any amount and type of data acquired in any technically feasible fashion. In at least one embodiment, self-modifying actions may relieve a data center operator of data center  800  from making possibly bad configuration decisions and possibly avoiding underutilized and/or poor performing portions of a data center. 
     In at least one embodiment, data center  800  may include tools, services, software or other resources to train one or more machine learning models or predict or infer information using one or more machine learning models according to one or more embodiments described herein. For example, in at least one embodiment, a machine learning model may be trained by calculating weight parameters according to a neural network architecture using software and computing resources described above with respect to data center  800 . In at least one embodiment, trained machine learning models corresponding to one or more neural networks may be used to infer or predict information using resources described above with respect to data center  800  by using weight parameters calculated through one or more training techniques described herein. 
     In at least one embodiment, data center may use CPUs, application-specific integrated circuits (ASICs), GPUs, FPGAs, or other hardware to perform training and/or inferencing using above-described resources. Moreover, one or more software and/or hardware resources described above may be configured as a service to allow users to train or performing inferencing of information, such as image recognition, speech recognition, or other artificial intelligence services. 
     Inference and/or training logic  715  are used to perform inferencing and/or training operations associated with one or more embodiments. Details regarding inference and/or training logic  715  are provided below in conjunction with  FIGS. 7A and/or 7B . In at least one embodiment, inference and/or training logic  715  may be used in system  FIG. 8  for inferencing or predicting operations based, at least in part, on weight parameters calculated using neural network training operations, neural network functions and/or architectures, or neural network use cases described herein. 
     Such components can be used to predict motion for generating animation. This can include using a motion prediction network trained as discussed herein to generate individual frames of animation. 
     Computer Systems 
       FIG. 9  is a block diagram illustrating an exemplary computer system, which may be a system with interconnected devices and components, a system-on-a-chip (SOC) or some combination thereof  900  formed with a processor that may include execution units to execute an instruction, according to at least one embodiment. In at least one embodiment, computer system  900  may include, without limitation, a component, such as a processor  902  to employ execution units including logic to perform algorithms for process data, in accordance with present disclosure, such as in embodiment described herein. In at least one embodiment, computer system  900  may include processors, such as PENTIUM® Processor family, Xeon™, Itanium®, XScale™ and/or StrongARM™, Intel® Core™, or Intel® Nervana™ microprocessors available from Intel Corporation of Santa Clara, Calif., although other systems (including PCs having other microprocessors, engineering workstations, set-top boxes and like) may also be used. In at least one embodiment, computer system  900  may execute a version of WINDOWS&#39; operating system available from Microsoft Corporation of Redmond, Wash., although other operating systems (UNIX and Linux for example), embedded software, and/or graphical user interfaces, may also be used. 
     Embodiments may be used in other devices such as handheld devices and embedded applications. Some examples of handheld devices include cellular phones, Internet Protocol devices, digital cameras, personal digital assistants (“PDAs”), and handheld PCs. In at least one embodiment, embedded applications may include a microcontroller, a digital signal processor (“DSP”), system on a chip, network computers (“NetPCs”), set-top boxes, network hubs, wide area network (“WAN”) switches, or any other system that may perform one or more instructions in accordance with at least one embodiment. 
     In at least one embodiment, computer system  900  may include, without limitation, processor  902  that may include, without limitation, one or more execution units  908  to perform machine learning model training and/or inferencing according to techniques described herein. In at least one embodiment, computer system  900  is a single processor desktop or server system, but in another embodiment computer system  900  may be a multiprocessor system. In at least one embodiment, processor  902  may include, without limitation, a complex instruction set computer (“CISC”) microprocessor, a reduced instruction set computing (“RISC”) microprocessor, a very long instruction word (“VLIW”) microprocessor, a processor implementing a combination of instruction sets, or any other processor device, such as a digital signal processor, for example. In at least one embodiment, processor  902  may be coupled to a processor bus  910  that may transmit data signals between processor  902  and other components in computer system  900 . 
     In at least one embodiment, processor  902  may include, without limitation, a Level 1 (“L1”) internal cache memory (“cache”)  904 . In at least one embodiment, processor  902  may have a single internal cache or multiple levels of internal cache. In at least one embodiment, cache memory may reside external to processor  902 . Other embodiments may also include a combination of both internal and external caches depending on particular implementation and needs. In at least one embodiment, register file  906  may store different types of data in various registers including, without limitation, integer registers, floating point registers, status registers, and instruction pointer register. 
     In at least one embodiment, execution unit  908 , including, without limitation, logic to perform integer and floating point operations, also resides in processor  902 . In at least one embodiment, processor  902  may also include a microcode (“ucode”) read only memory (“ROM”) that stores microcode for certain macro instructions. In at least one embodiment, execution unit  908  may include logic to handle a packed instruction set  909 . In at least one embodiment, by including packed instruction set  909  in an instruction set of a general-purpose processor  902 , along with associated circuitry to execute instructions, operations used by many multimedia applications may be performed using packed data in a general-purpose processor  902 . In one or more embodiments, many multimedia applications may be accelerated and executed more efficiently by using full width of a processor&#39;s data bus for performing operations on packed data, which may eliminate need to transfer smaller units of data across processor&#39;s data bus to perform one or more operations one data element at a time. 
     In at least one embodiment, execution unit  908  may also be used in microcontrollers, embedded processors, graphics devices, DSPs, and other types of logic circuits. In at least one embodiment, computer system  900  may include, without limitation, a memory  920 . In at least one embodiment, memory  920  may be implemented as a Dynamic Random Access Memory (“DRAM”) device, a Static Random Access Memory (“SRAM”) device, flash memory device, or other memory device. In at least one embodiment, memory  920  may store instruction(s)  919  and/or data  921  represented by data signals that may be executed by processor  902 . 
     In at least one embodiment, system logic chip may be coupled to processor bus  910  and memory  920 . In at least one embodiment, system logic chip may include, without limitation, a memory controller hub (“MCH”)  916 , and processor  902  may communicate with MCH  916  via processor bus  910 . In at least one embodiment, MCH  916  may provide a high bandwidth memory path  918  to memory  920  for instruction and data storage and for storage of graphics commands, data and textures. In at least one embodiment, MCH  916  may direct data signals between processor  902 , memory  920 , and other components in computer system  900  and to bridge data signals between processor bus  910 , memory  920 , and a system I/O  922 . In at least one embodiment, system logic chip may provide a graphics port for coupling to a graphics controller. In at least one embodiment, MCH  916  may be coupled to memory  920  through a high bandwidth memory path  918  and graphics/video card  912  may be coupled to MCH  916  through an Accelerated Graphics Port (“AGP”) interconnect  914 . 
     In at least one embodiment, computer system  900  may use system I/O  922  that is a proprietary hub interface bus to couple MCH  916  to I/O controller hub (“ICH”)  930 . In at least one embodiment, ICH  930  may provide direct connections to some I/O devices via a local I/O bus. In at least one embodiment, local I/O bus may include, without limitation, a high-speed I/O bus for connecting peripherals to memory  920 , chipset, and processor  902 . Examples may include, without limitation, an audio controller  929 , a firmware hub (“flash BIOS”)  928 , a wireless transceiver  926 , a data storage  924 , a legacy I/O controller  923  containing user input and keyboard interfaces  925 , a serial expansion port  927 , such as Universal Serial Bus (“USB”), and a network controller  934 . Data storage  924  may comprise a hard disk drive, a floppy disk drive, a CD-ROM device, a flash memory device, or other mass storage device. 
     In at least one embodiment,  FIG. 9  illustrates a system, which includes interconnected hardware devices or “chips”, whereas in other embodiments,  FIG. 9  may illustrate an exemplary System on a Chip (“SoC”). In at least one embodiment, devices may be interconnected with proprietary interconnects, standardized interconnects (e.g., PCIe) or some combination thereof. In at least one embodiment, one or more components of computer system  900  are interconnected using compute express link (CXL) interconnects. 
     Inference and/or training logic  715  are used to perform inferencing and/or training operations associated with one or more embodiments. Details regarding inference and/or training logic  715  are provided below in conjunction with  FIGS. 7A and/or 7B . In at least one embodiment, inference and/or training logic  715  may be used in system  FIG. 9  for inferencing or predicting operations based, at least in part, on weight parameters calculated using neural network training operations, neural network functions and/or architectures, or neural network use cases described herein. 
     Such components can be used to predict motion for generating animation. This can include using a motion prediction network trained as discussed herein to generate individual frames of animation. 
       FIG. 10  is a block diagram illustrating an electronic device  1000  for utilizing a processor  1010 , according to at least one embodiment. In at least one embodiment, electronic device  1000  may be, for example and without limitation, a notebook, a tower server, a rack server, a blade server, a laptop, a desktop, a tablet, a mobile device, a phone, an embedded computer, or any other suitable electronic device. 
     In at least one embodiment, system  1000  may include, without limitation, processor  1010  communicatively coupled to any suitable number or kind of components, peripherals, modules, or devices. In at least one embodiment, processor  1010  coupled using a bus or interface, such as a 1° C. bus, a System Management Bus (“SMBus”), a Low Pin Count (LPC) bus, a Serial Peripheral Interface (“SPI”), a High Definition Audio (“HDA”) bus, a Serial Advance Technology Attachment (“SATA”) bus, a Universal Serial Bus (“USB”) (versions 1, 2, 3), or a Universal Asynchronous Receiver/Transmitter (“UART”) bus. In at least one embodiment,  FIG. 10  illustrates a system, which includes interconnected hardware devices or “chips”, whereas in other embodiments,  FIG. 10  may illustrate an exemplary System on a Chip (“SoC”). In at least one embodiment, devices illustrated in  FIG. 10  may be interconnected with proprietary interconnects, standardized interconnects (e.g., PCIe) or some combination thereof. In at least one embodiment, one or more components of  FIG. 10  are interconnected using compute express link (CXL) interconnects. 
     In at least one embodiment,  FIG. 10  may include a display  1024 , a touch screen  1025 , a touch pad  1030 , a Near Field Communications unit (“NFC”)  1045 , a sensor hub  1040 , a thermal sensor  1046 , an Express Chipset (“EC”)  1035 , a Trusted Platform Module (“TPM”)  1038 , BIOS/firmware/flash memory (“BIOS, FW Flash”)  1022 , a DSP  1060 , a drive  1020  such as a Solid State Disk (“SSD”) or a Hard Disk Drive (“HDD”), a wireless local area network unit (“WLAN”)  1050 , a Bluetooth unit  1052 , a Wireless Wide Area Network unit (“WWAN”)  1056 , a Global Positioning System (GPS)  1055 , a camera (“USB 3.0 camera”)  1054  such as a USB 3.0 camera, and/or a Low Power Double Data Rate (“LPDDR”) memory unit (“LPDDR3”)  1015  implemented in, for example, LPDDR3 standard. These components may each be implemented in any suitable manner. 
     In at least one embodiment, other components may be communicatively coupled to processor  1010  through components discussed above. In at least one embodiment, an accelerometer  1041 , Ambient Light Sensor (“ALS”)  1042 , compass  1043 , and a gyroscope  1044  may be communicatively coupled to sensor hub  1040 . In at least one embodiment, thermal sensor  1039 , a fan  1037 , a keyboard  1046 , and a touch pad  1030  may be communicatively coupled to EC  1035 . In at least one embodiment, speaker  1063 , headphones  1064 , and microphone (“mic”)  1065  may be communicatively coupled to an audio unit (“audio codec and class d amp”)  1062 , which may in turn be communicatively coupled to DSP  1060 . In at least one embodiment, audio unit  1064  may include, for example and without limitation, an audio coder/decoder (“codec”) and a class D amplifier. In at least one embodiment, SIM card (“SIM”)  1057  may be communicatively coupled to WWAN unit  1056 . In at least one embodiment, components such as WLAN unit  1050  and Bluetooth unit  1052 , as well as WWAN unit  1056  may be implemented in a Next Generation Form Factor (“NGFF”). 
     Inference and/or training logic  715  are used to perform inferencing and/or training operations associated with one or more embodiments. Details regarding inference and/or training logic  715  are provided below in conjunction with  FIGS. 7 a    and/or  7   b . In at least one embodiment, inference and/or training logic  715  may be used in system  FIG. 10  for inferencing or predicting operations based, at least in part, on weight parameters calculated using neural network training operations, neural network functions and/or architectures, or neural network use cases described herein. 
     Such components can be used to predict motion for generating animation. This can include using a motion prediction network trained as discussed herein to generate individual frames of animation. 
       FIG. 11  is a block diagram of a processing system, according to at least one embodiment. In at least one embodiment, system  1100  includes one or more processors  1102  and one or more graphics processors  1108 , and may be a single processor desktop system, a multiprocessor workstation system, or a server system having a large number of processors  1102  or processor cores  1107 . In at least one embodiment, system  1100  is a processing platform incorporated within a system-on-a-chip (SoC) integrated circuit for use in mobile, handheld, or embedded devices. 
     In at least one embodiment, system  1100  can include, or be incorporated within a server-based gaming platform, a game console, including a game and media console, a mobile gaming console, a handheld game console, or an online game console. In at least one embodiment, system  1100  is a mobile phone, smart phone, tablet computing device or mobile Internet device. In at least one embodiment, processing system  1100  can also include, couple with, or be integrated within a wearable device, such as a smart watch wearable device, smart eyewear device, augmented reality device, or virtual reality device. In at least one embodiment, processing system  1100  is a television or set top box device having one or more processors  1102  and a graphical interface generated by one or more graphics processors  1108 . 
     In at least one embodiment, one or more processors  1102  each include one or more processor cores  1107  to process instructions which, when executed, perform operations for system and user software. In at least one embodiment, each of one or more processor cores  1107  is configured to process a specific instruction set  1109 . In at least one embodiment, instruction set  1109  may facilitate Complex Instruction Set Computing (CISC), Reduced Instruction Set Computing (RISC), or computing via a Very Long Instruction Word (VLIW). In at least one embodiment, processor cores  1107  may each process a different instruction set  1109 , which may include instructions to facilitate emulation of other instruction sets. In at least one embodiment, processor core  1107  may also include other processing devices, such a Digital Signal Processor (DSP). 
     In at least one embodiment, processor  1102  includes cache memory  1104 . In at least one embodiment, processor  1102  can have a single internal cache or multiple levels of internal cache. In at least one embodiment, cache memory is shared among various components of processor  1102 . In at least one embodiment, processor  1102  also uses an external cache (e.g., a Level-3 (L3) cache or Last Level Cache (LLC)) (not shown), which may be shared among processor cores  1107  using known cache coherency techniques. In at least one embodiment, register file  1106  is additionally included in processor  1102  which may include different types of registers for storing different types of data (e.g., integer registers, floating point registers, status registers, and an instruction pointer register). In at least one embodiment, register file  1106  may include general-purpose registers or other registers. 
     In at least one embodiment, one or more processor(s)  1102  are coupled with one or more interface bus(es)  1110  to transmit communication signals such as address, data, or control signals between processor  1102  and other components in system  1100 . In at least one embodiment, interface bus  1110 , in one embodiment, can be a processor bus, such as a version of a Direct Media Interface (DMI) bus. In at least one embodiment, interface  1110  is not limited to a DMI bus, and may include one or more Peripheral Component Interconnect buses (e.g., PCI, PCI Express), memory busses, or other types of interface busses. In at least one embodiment processor(s)  1102  include an integrated memory controller  1116  and a platform controller hub  1130 . In at least one embodiment, memory controller  1116  facilitates communication between a memory device and other components of system  1100 , while platform controller hub (PCH)  1130  provides connections to I/O devices via a local I/O bus. 
     In at least one embodiment, memory device  1120  can be a dynamic random access memory (DRAM) device, a static random access memory (SRAM) device, flash memory device, phase-change memory device, or some other memory device having suitable performance to serve as process memory. In at least one embodiment memory device  1120  can operate as system memory for system  1100 , to store data  1122  and instructions  1121  for use when one or more processors  1102  executes an application or process. In at least one embodiment, memory controller  1116  also couples with an optional external graphics processor  1112 , which may communicate with one or more graphics processors  1108  in processors  1102  to perform graphics and media operations. In at least one embodiment, a display device  1111  can connect to processor(s)  1102 . In at least one embodiment display device  1111  can include one or more of an internal display device, as in a mobile electronic device or a laptop device or an external display device attached via a display interface (e.g., DisplayPort, etc.). In at least one embodiment, display device  1111  can include a head mounted display (HMD) such as a stereoscopic display device for use in virtual reality (VR) applications or augmented reality (AR) applications. 
     In at least one embodiment, platform controller hub  1130  enables peripherals to connect to memory device  1120  and processor  1102  via a high-speed I/O bus. In at least one embodiment, I/O peripherals include, but are not limited to, an audio controller  1146 , a network controller  1134 , a firmware interface  1128 , a wireless transceiver  1126 , touch sensors  1125 , a data storage device  1124  (e.g., hard disk drive, flash memory, etc.). In at least one embodiment, data storage device  1124  can connect via a storage interface (e.g., SATA) or via a peripheral bus, such as a Peripheral Component Interconnect bus (e.g., PCI, PCI Express). In at least one embodiment, touch sensors  1125  can include touch screen sensors, pressure sensors, or fingerprint sensors. In at least one embodiment, wireless transceiver  1126  can be a Wi-Fi transceiver, a Bluetooth transceiver, or a mobile network transceiver such as a 3G, 4G, or Long Term Evolution (LTE) transceiver. In at least one embodiment, firmware interface  1128  enables communication with system firmware, and can be, for example, a unified extensible firmware interface (UEFI). In at least one embodiment, network controller  1134  can enable a network connection to a wired network. In at least one embodiment, a high-performance network controller (not shown) couples with interface bus  1110 . In at least one embodiment, audio controller  1146  is a multi-channel high definition audio controller. In at least one embodiment, system  1100  includes an optional legacy I/O controller  1140  for coupling legacy (e.g., Personal System 2 (PS/2)) devices to system. In at least one embodiment, platform controller hub  1130  can also connect to one or more Universal Serial Bus (USB) controllers  1142  connect input devices, such as keyboard and mouse  1143  combinations, a camera  1144 , or other USB input devices. 
     In at least one embodiment, an instance of memory controller  1116  and platform controller hub  1130  may be integrated into a discreet external graphics processor, such as external graphics processor  1112 . In at least one embodiment, platform controller hub  1130  and/or memory controller  1116  may be external to one or more processor(s)  1102 . For example, in at least one embodiment, system  1100  can include an external memory controller  1116  and platform controller hub  1130 , which may be configured as a memory controller hub and peripheral controller hub within a system chipset that is in communication with processor(s)  1102 . 
     Inference and/or training logic  715  are used to perform inferencing and/or training operations associated with one or more embodiments. Details regarding inference and/or training logic  715  are provided below in conjunction with  FIGS. 7A and/or 7B . In at least one embodiment portions or all of inference and/or training logic  715  may be incorporated into graphics processor  1500 . For example, in at least one embodiment, training and/or inferencing techniques described herein may use one or more of ALUs embodied in a graphics processor. Moreover, in at least one embodiment, inferencing and/or training operations described herein may be done using logic other than logic illustrated in  FIGS. 7A or 7B . In at least one embodiment, weight parameters may be stored in on-chip or off-chip memory and/or registers (shown or not shown) that configure ALUs of a graphics processor to perform one or more machine learning algorithms, neural network architectures, use cases, or training techniques described herein. 
     Such components can be used to predict motion for generating animation. This can include using a motion prediction network trained as discussed herein to generate individual frames of animation. 
       FIG. 12  is a block diagram of a processor  1200  having one or more processor cores  1202 A- 1202 N, an integrated memory controller  1214 , and an integrated graphics processor  1208 , according to at least one embodiment. In at least one embodiment, processor  1200  can include additional cores up to and including additional core  1202 N represented by dashed lined boxes. In at least one embodiment, each of processor cores  1202 A- 1202 N includes one or more internal cache units  1204 A- 1204 N. In at least one embodiment, each processor core also has access to one or more shared cached units  1206 . 
     In at least one embodiment, internal cache units  1204 A- 1204 N and shared cache units  1206  represent a cache memory hierarchy within processor  1200 . In at least one embodiment, cache memory units  1204 A- 1204 N may include at least one level of instruction and data cache within each processor core and one or more levels of shared mid-level cache, such as a Level 2 (L2), Level 3 (L3), Level 4 (L4), or other levels of cache, where a highest level of cache before external memory is classified as an LLC. In at least one embodiment, cache coherency logic maintains coherency between various cache units  1206  and  1204 A- 1204 N. 
     In at least one embodiment, processor  1200  may also include a set of one or more bus controller units  1216  and a system agent core  1210 . In at least one embodiment, one or more bus controller units  1216  manage a set of peripheral buses, such as one or more PCI or PCI express busses. In at least one embodiment, system agent core  1210  provides management functionality for various processor components. In at least one embodiment, system agent core  1210  includes one or more integrated memory controllers  1214  to manage access to various external memory devices (not shown). 
     In at least one embodiment, one or more of processor cores  1202 A- 1202 N include support for simultaneous multi-threading. In at least one embodiment, system agent core  1210  includes components for coordinating and operating cores  1202 A- 1202 N during multi-threaded processing. In at least one embodiment, system agent core  1210  may additionally include a power control unit (PCU), which includes logic and components to regulate one or more power states of processor cores  1202 A- 1202 N and graphics processor  1208 . 
     In at least one embodiment, processor  1200  additionally includes graphics processor  1208  to execute graphics processing operations. In at least one embodiment, graphics processor  1208  couples with shared cache units  1206 , and system agent core  1210 , including one or more integrated memory controllers  1214 . In at least one embodiment, system agent core  1210  also includes a display controller  1211  to drive graphics processor output to one or more coupled displays. In at least one embodiment, display controller  1211  may also be a separate module coupled with graphics processor  1208  via at least one interconnect, or may be integrated within graphics processor  1208 . 
     In at least one embodiment, a ring based interconnect unit  1212  is used to couple internal components of processor  1200 . In at least one embodiment, an alternative interconnect unit may be used, such as a point-to-point interconnect, a switched interconnect, or other techniques. In at least one embodiment, graphics processor  1208  couples with ring interconnect  1212  via an I/O link  1213 . 
     In at least one embodiment, I/O link  1213  represents at least one of multiple varieties of I/O interconnects, including an on package I/O interconnect which facilitates communication between various processor components and a high-performance embedded memory module  1218 , such as an eDRAM module. In at least one embodiment, each of processor cores  1202 A- 1202 N and graphics processor  1208  use embedded memory modules  1218  as a shared Last Level Cache. 
     In at least one embodiment, processor cores  1202 A- 1202 N are homogenous cores executing a common instruction set architecture. In at least one embodiment, processor cores  1202 A- 1202 N are heterogeneous in terms of instruction set architecture (ISA), where one or more of processor cores  1202 A- 1202 N execute a common instruction set, while one or more other cores of processor cores  1202 A- 1202 N executes a subset of a common instruction set or a different instruction set. In at least one embodiment, processor cores  1202 A- 1202 N are heterogeneous in terms of microarchitecture, where one or more cores having a relatively higher power consumption couple with one or more power cores having a lower power consumption. In at least one embodiment, processor  1200  can be implemented on one or more chips or as an SoC integrated circuit. 
     Inference and/or training logic  715  are used to perform inferencing and/or training operations associated with one or more embodiments. Details regarding inference and/or training logic  715  are provided below in conjunction with  FIGS. 7 a    and/or  7   b . In at least one embodiment portions or all of inference and/or training logic  715  may be incorporated into processor  1200 . For example, in at least one embodiment, training and/or inferencing techniques described herein may use one or more of ALUs embodied in graphics processor  1512 , graphics core(s)  1202 A- 1202 N, or other components in  FIG. 12 . Moreover, in at least one embodiment, inferencing and/or training operations described herein may be done using logic other than logic illustrated in  FIGS. 7A or 7B . In at least one embodiment, weight parameters may be stored in on-chip or off-chip memory and/or registers (shown or not shown) that configure ALUs of graphics processor  1200  to perform one or more machine learning algorithms, neural network architectures, use cases, or training techniques described herein. 
     Such components can be used to predict motion for generating animation. This can include using a motion prediction network trained as discussed herein to generate individual frames of animation. 
     Virtualized Computing Platform 
       FIG. 13  is an example data flow diagram for a process  1300  of generating and deploying a frame processing and inferencing pipeline, in accordance with at least one embodiment. In at least one embodiment, process  1300  may be deployed for use with imaging devices, processing devices, and/or other device types at one or more facilities  1302 . Process  1300  may be executed within a training system  1304  and/or a deployment system  1306 . In at least one embodiment, training system  1304  may be used to perform training, deployment, and implementation of machine learning models (e.g., neural networks, object detection algorithms, computer vision algorithms, etc.) for use in deployment system  1306 . In at least one embodiment, deployment system  1306  may be configured to offload processing and compute resources among a distributed computing environment to reduce infrastructure requirements at facility  1302 . In at least one embodiment, one or more applications in a pipeline may use or call upon services (e.g., inference, visualization, compute, AI, etc.) of deployment system  1306  during execution of applications. 
     In at least one embodiment, some of applications used in advanced processing and inferencing pipelines may use machine learning models or other AI to perform one or more processing steps. In at least one embodiment, machine learning models may be trained at facility  1302  using data  1308  (such as imaging data) generated at facility  1302  (and stored on one or more picture archiving and communication system (PACS) servers at facility  1302 ), may be trained using imaging or sequencing data  1308  from another facility(ies), or a combination thereof. In at least one embodiment, training system  1304  may be used to provide applications, services, and/or other resources for generating working, deployable machine learning models for deployment system  1306 . 
     In at least one embodiment, model registry  1324  may be backed by object storage that may support versioning and object metadata. In at least one embodiment, object storage may be accessible through, for example, a cloud storage (e.g., cloud  1426  of  FIG. 14 ) compatible application programming interface (API) from within a cloud platform. In at least one embodiment, machine learning models within model registry  1324  may uploaded, listed, modified, or deleted by developers or partners of a system interacting with an API. In at least one embodiment, an API may provide access to methods that allow users with appropriate credentials to associate models with applications, such that models may be executed as part of execution of containerized instantiations of applications. 
     In at least one embodiment, training pipeline  1404  ( FIG. 14 ) may include a scenario where facility  1302  is training their own machine learning model, or has an existing machine learning model that needs to be optimized or updated. In at least one embodiment, imaging data  1308  generated by imaging device(s), sequencing devices, and/or other device types may be received. In at least one embodiment, once imaging data  1308  is received, AI-assisted annotation  1310  may be used to aid in generating annotations corresponding to imaging data  1308  to be used as ground truth data for a machine learning model. In at least one embodiment, AI-assisted annotation  1310  may include one or more machine learning models (e.g., convolutional neural networks (CNNs)) that may be trained to generate annotations corresponding to certain types of imaging data  1308  (e.g., from certain devices). In at least one embodiment, AI-assisted annotations  1310  may then be used directly, or may be adjusted or fine-tuned using an annotation tool to generate ground truth data. In at least one embodiment, AI-assisted annotations  1310 , labeled clinic data  1312 , or a combination thereof may be used as ground truth data for training a machine learning model. In at least one embodiment, a trained machine learning model may be referred to as output model  1316 , and may be used by deployment system  1306 , as described herein. 
     In at least one embodiment, training pipeline  1404  ( FIG. 14 ) may include a scenario where facility  1302  needs a machine learning model for use in performing one or more processing tasks for one or more applications in deployment system  1306 , but facility  1302  may not currently have such a machine learning model (or may not have a model that is optimized, efficient, or effective for such purposes). In at least one embodiment, an existing machine learning model may be selected from a model registry  1324 . In at least one embodiment, model registry  1324  may include machine learning models trained to perform a variety of different inference tasks on imaging data. In at least one embodiment, machine learning models in model registry  1324  may have been trained on imaging data from different facilities than facility  1302  (e.g., facilities remotely located). In at least one embodiment, machine learning models may have been trained on imaging data from one location, two locations, or any number of locations. In at least one embodiment, when being trained on imaging data from a specific location, training may take place at that location, or at least in a manner that protects confidentiality of imaging data or restricts imaging data from being transferred off-premises. In at least one embodiment, once a model is trained—or partially trained—at one location, a machine learning model may be added to model registry  1324 . In at least one embodiment, a machine learning model may then be retrained, or updated, at any number of other facilities, and a retrained or updated model may be made available in model registry  1324 . In at least one embodiment, a machine learning model may then be selected from model registry  1324 —and referred to as output model  1316 —and may be used in deployment system  1306  to perform one or more processing tasks for one or more applications of a deployment system. 
     In at least one embodiment, training pipeline  1404  ( FIG. 14 ), a scenario may include facility  1302  requiring a machine learning model for use in performing one or more processing tasks for one or more applications in deployment system  1306 , but facility  1302  may not currently have such a machine learning model (or may not have a model that is optimized, efficient, or effective for such purposes). In at least one embodiment, a machine learning model selected from model registry  1324  may not be fine-tuned or optimized for imaging data  1308  generated at facility  1302  because of differences in populations, robustness of training data used to train a machine learning model, diversity in anomalies of training data, and/or other issues with training data. In at least one embodiment, AI-assisted annotation  1310  may be used to aid in generating annotations corresponding to imaging data  1308  to be used as ground truth data for retraining or updating a machine learning model. In at least one embodiment, labeled data  1312  may be used as ground truth data for training a machine learning model. In at least one embodiment, retraining or updating a machine learning model may be referred to as model training  1314 . In at least one embodiment, model training  1314 —e.g., AI-assisted annotations  1310 , labeled clinic data  1312 , or a combination thereof—may be used as ground truth data for retraining or updating a machine learning model. In at least one embodiment, a trained machine learning model may be referred to as output model  1316 , and may be used by deployment system  1306 , as described herein. 
     In at least one embodiment, deployment system  1306  may include software  1318 , services  1320 , hardware  1322 , and/or other components, features, and functionality. In at least one embodiment, deployment system  1306  may include a software “stack,” such that software  1318  may be built on top of services  1320  and may use services  1320  to perform some or all of processing tasks, and services  1320  and software  1318  may be built on top of hardware  1322  and use hardware  1322  to execute processing, storage, and/or other compute tasks of deployment system  1306 . In at least one embodiment, software  1318  may include any number of different containers, where each container may execute an instantiation of an application. In at least one embodiment, each application may perform one or more processing tasks in an advanced processing and inferencing pipeline (e.g., inferencing, object detection, feature detection, segmentation, image enhancement, calibration, etc.). In at least one embodiment, an advanced processing and inferencing pipeline may be defined based on selections of different containers that are desired or required for processing imaging data  1308 , in addition to containers that receive and configure imaging data for use by each container and/or for use by facility  1302  after processing through a pipeline (e.g., to convert outputs back to a usable data type). In at least one embodiment, a combination of containers within software  1318  (e.g., that make up a pipeline) may be referred to as a virtual instrument (as described in more detail herein), and a virtual instrument may leverage services  1320  and hardware  1322  to execute some or all processing tasks of applications instantiated in containers. 
     In at least one embodiment, a data processing pipeline may receive input data (e.g., imaging data  1308 ) in a specific format in response to an inference request (e.g., a request from a user of deployment system  1306 ). In at least one embodiment, input data may be representative of one or more images, video, and/or other data representations generated by one or more imaging devices. In at least one embodiment, data may undergo pre-processing as part of data processing pipeline to prepare data for processing by one or more applications. In at least one embodiment, post-processing may be performed on an output of one or more inferencing tasks or other processing tasks of a pipeline to prepare an output data for a next application and/or to prepare output data for transmission and/or use by a user (e.g., as a response to an inference request). In at least one embodiment, inferencing tasks may be performed by one or more machine learning models, such as trained or deployed neural networks, which may include output models  1316  of training system  1304 . 
     In at least one embodiment, tasks of data processing pipeline may be encapsulated in a container(s) that each represent a discrete, fully functional instantiation of an application and virtualized computing environment that is able to reference machine learning models. In at least one embodiment, containers or applications may be published into a private (e.g., limited access) area of a container registry (described in more detail herein), and trained or deployed models may be stored in model registry  1324  and associated with one or more applications. In at least one embodiment, images of applications (e.g., container images) may be available in a container registry, and once selected by a user from a container registry for deployment in a pipeline, an image may be used to generate a container for an instantiation of an application for use by a user&#39;s system. 
     In at least one embodiment, developers (e.g., software developers, clinicians, doctors, etc.) may develop, publish, and store applications (e.g., as containers) for performing image processing and/or inferencing on supplied data. In at least one embodiment, development, publishing, and/or storing may be performed using a software development kit (SDK) associated with a system (e.g., to ensure that an application and/or container developed is compliant with or compatible with a system). In at least one embodiment, an application that is developed may be tested locally (e.g., at a first facility, on data from a first facility) with an SDK which may support at least some of services  1320  as a system (e.g., system  1400  of  FIG. 14 ). In at least one embodiment, because DICOM objects may contain anywhere from one to hundreds of images or other data types, and due to a variation in data, a developer may be responsible for managing (e.g., setting constructs for, building pre-processing into an application, etc.) extraction and preparation of incoming data. In at least one embodiment, once validated by system  1400  (e.g., for accuracy), an application may be available in a container registry for selection and/or implementation by a user to perform one or more processing tasks with respect to data at a facility (e.g., a second facility) of a user. 
     In at least one embodiment, developers may then share applications or containers through a network for access and use by users of a system (e.g., system  1400  of  FIG. 14 ). In at least one embodiment, completed and validated applications or containers may be stored in a container registry and associated machine learning models may be stored in model registry  1324 . In at least one embodiment, a requesting entity—who provides an inference or image processing request—may browse a container registry and/or model registry  1324  for an application, container, dataset, machine learning model, etc., select a desired combination of elements for inclusion in data processing pipeline, and submit an imaging processing request. In at least one embodiment, a request may include input data (and associated patient data, in some examples) that is necessary to perform a request, and/or may include a selection of application(s) and/or machine learning models to be executed in processing a request. In at least one embodiment, a request may then be passed to one or more components of deployment system  1306  (e.g., a cloud) to perform processing of data processing pipeline. In at least one embodiment, processing by deployment system  1306  may include referencing selected elements (e.g., applications, containers, models, etc.) from a container registry and/or model registry  1324 . In at least one embodiment, once results are generated by a pipeline, results may be returned to a user for reference (e.g., for viewing in a viewing application suite executing on a local, on-premises workstation or terminal). In at least one embodiment, a radiologist may receive results from an data processing pipeline including any number of application and/or containers, where results may include anomaly detection in X-rays, CT scans, MRIs, etc. 
     In at least one embodiment, to aid in processing or execution of applications or containers in pipelines, services  1320  may be leveraged. In at least one embodiment, services  1320  may include compute services, artificial intelligence (AI) services, visualization services, and/or other service types. In at least one embodiment, services  1320  may provide functionality that is common to one or more applications in software  1318 , so functionality may be abstracted to a service that may be called upon or leveraged by applications. In at least one embodiment, functionality provided by services  1320  may run dynamically and more efficiently, while also scaling well by allowing applications to process data in parallel (e.g., using a parallel computing platform  1430  ( FIG. 14 )). In at least one embodiment, rather than each application that shares a same functionality offered by a service  1320  being required to have a respective instance of service  1320 , service  1320  may be shared between and among various applications. In at least one embodiment, services may include an inference server or engine that may be used for executing detection or segmentation tasks, as non-limiting examples. In at least one embodiment, a model training service may be included that may provide machine learning model training and/or retraining capabilities. In at least one embodiment, a data augmentation service may further be included that may provide GPU accelerated data (e.g., DICOM, RIS, CIS, REST compliant, RPC, raw, etc.) extraction, resizing, scaling, and/or other augmentation. In at least one embodiment, a visualization service may be used that may add image rendering effects—such as ray-tracing, rasterization, denoising, sharpening, etc.—to add realism to two-dimensional (2D) and/or three-dimensional (3D) models. In at least one embodiment, virtual instrument services may be included that provide for beam-forming, segmentation, inferencing, imaging, and/or support for other applications within pipelines of virtual instruments. 
     In at least one embodiment, where a service  1320  includes an AI service (e.g., an inference service), one or more machine learning models may be executed by calling upon (e.g., as an API call) an inference service (e.g., an inference server) to execute machine learning model(s), or processing thereof, as part of application execution. In at least one embodiment, where another application includes one or more machine learning models for segmentation tasks, an application may call upon an inference service to execute machine learning models for performing one or more of processing operations associated with segmentation tasks. In at least one embodiment, software  1318  implementing advanced processing and inferencing pipeline that includes segmentation application and anomaly detection application may be streamlined because each application may call upon a same inference service to perform one or more inferencing tasks. 
     In at least one embodiment, hardware  1322  may include GPUs, CPUs, graphics cards, an AI/deep learning system (e.g., an AI supercomputer, such as NVIDIA&#39;s DGX), a cloud platform, or a combination thereof. In at least one embodiment, different types of hardware  1322  may be used to provide efficient, purpose-built support for software  1318  and services  1320  in deployment system  1306 . In at least one embodiment, use of GPU processing may be implemented for processing locally (e.g., at facility  1302 ), within an AI/deep learning system, in a cloud system, and/or in other processing components of deployment system  1306  to improve efficiency, accuracy, and efficacy of image processing and generation. In at least one embodiment, software  1318  and/or services  1320  may be optimized for GPU processing with respect to deep learning, machine learning, and/or high-performance computing, as non-limiting examples. In at least one embodiment, at least some of computing environment of deployment system  1306  and/or training system  1304  may be executed in a datacenter one or more supercomputers or high performance computing systems, with GPU optimized software (e.g., hardware and software combination of NVIDIA&#39;s DGX System). In at least one embodiment, hardware  1322  may include any number of GPUs that may be called upon to perform processing of data in parallel, as described herein. In at least one embodiment, cloud platform may further include GPU processing for GPU-optimized execution of deep learning tasks, machine learning tasks, or other computing tasks. In at least one embodiment, cloud platform (e.g., NVIDIA&#39;s NGC) may be executed using an AI/deep learning supercomputer(s) and/or GPU-optimized software (e.g., as provided on NVIDIA&#39;s DGX Systems) as a hardware abstraction and scaling platform. In at least one embodiment, cloud platform may integrate an application container clustering system or orchestration system (e.g., KUBERNETES) on multiple GPUs to enable seamless scaling and load balancing. 
       FIG. 14  is a system diagram for an example system  1400  for generating and deploying an imaging deployment pipeline, in accordance with at least one embodiment. In at least one embodiment, system  1400  may be used to implement process  1300  of  FIG. 13  and/or other processes including advanced processing and inferencing pipelines. In at least one embodiment, system  1400  may include training system  1304  and deployment system  1306 . In at least one embodiment, training system  1304  and deployment system  1306  may be implemented using software  1318 , services  1320 , and/or hardware  1322 , as described herein. 
     In at least one embodiment, system  1400  (e.g., training system  1304  and/or deployment system  1306 ) may implemented in a cloud computing environment (e.g., using cloud  1426 ). In at least one embodiment, system  1400  may be implemented locally with respect to a healthcare services facility, or as a combination of both cloud and local computing resources. In at least one embodiment, access to APIs in cloud  1426  may be restricted to authorized users through enacted security measures or protocols. In at least one embodiment, a security protocol may include web tokens that may be signed by an authentication (e.g., AuthN, AuthZ, Gluecon, etc.) service and may carry appropriate authorization. In at least one embodiment, APIs of virtual instruments (described herein), or other instantiations of system  1400 , may be restricted to a set of public IPs that have been vetted or authorized for interaction. 
     In at least one embodiment, various components of system  1400  may communicate between and among one another using any of a variety of different network types, including but not limited to local area networks (LANs) and/or wide area networks (WANs) via wired and/or wireless communication protocols. In at least one embodiment, communication between facilities and components of system  1400  (e.g., for transmitting inference requests, for receiving results of inference requests, etc.) may be communicated over data bus(ses), wireless data protocols (Wi-Fi), wired data protocols (e.g., Ethernet), etc. 
     In at least one embodiment, training system  1304  may execute training pipelines  1404 , similar to those described herein with respect to  FIG. 13 . In at least one embodiment, where one or more machine learning models are to be used in deployment pipelines  1410  by deployment system  1306 , training pipelines  1404  may be used to train or retrain one or more (e.g. pre-trained) models, and/or implement one or more of pre-trained models  1406  (e.g., without a need for retraining or updating). In at least one embodiment, as a result of training pipelines  1404 , output model(s)  1316  may be generated. In at least one embodiment, training pipelines  1404  may include any number of processing steps, such as but not limited to imaging data (or other input data) conversion or adaption In at least one embodiment, for different machine learning models used by deployment system  1306 , different training pipelines  1404  may be used. In at least one embodiment, training pipeline  1404  similar to a first example described with respect to  FIG. 13  may be used for a first machine learning model, training pipeline  1404  similar to a second example described with respect to  FIG. 13  may be used for a second machine learning model, and training pipeline  1404  similar to a third example described with respect to  FIG. 13  may be used for a third machine learning model. In at least one embodiment, any combination of tasks within training system  1304  may be used depending on what is required for each respective machine learning model. In at least one embodiment, one or more of machine learning models may already be trained and ready for deployment so machine learning models may not undergo any processing by training system  1304 , and may be implemented by deployment system  1306 . 
     In at least one embodiment, output model(s)  1316  and/or pre-trained model(s)  1406  may include any types of machine learning models depending on implementation or embodiment. In at least one embodiment, and without limitation, machine learning models used by system  1400  may include machine learning model(s) using linear regression, logistic regression, decision trees, support vector machines (SVM), Naïve Bayes, k-nearest neighbor (Knn), K means clustering, random forest, dimensionality reduction algorithms, gradient boosting algorithms, neural networks (e.g., auto-encoders, convolutional, recurrent, perceptrons, Long/Short Term Memory (LSTM), Hopfield, Boltzmann, deep belief, deconvolutional, generative adversarial, liquid state machine, etc.), and/or other types of machine learning models. 
     In at least one embodiment, training pipelines  1404  may include AI-assisted annotation, as described in more detail herein with respect to at least  FIG. 15B . In at least one embodiment, labeled data  1312  (e.g., traditional annotation) may be generated by any number of techniques. In at least one embodiment, labels or other annotations may be generated within a drawing program (e.g., an annotation program), a computer aided design (CAD) program, a labeling program, another type of program suitable for generating annotations or labels for ground truth, and/or may be hand drawn, in some examples. In at least one embodiment, ground truth data may be synthetically produced (e.g., generated from computer models or renderings), real produced (e.g., designed and produced from real-world data), machine-automated (e.g., using feature analysis and learning to extract features from data and then generate labels), human annotated (e.g., labeler, or annotation expert, defines location of labels), and/or a combination thereof. In at least one embodiment, for each instance of imaging data  1308  (or other data type used by machine learning models), there may be corresponding ground truth data generated by training system  1304 . In at least one embodiment, AI-assisted annotation may be performed as part of deployment pipelines  1410 ; either in addition to, or in lieu of AI-assisted annotation included in training pipelines  1404 . In at least one embodiment, system  1400  may include a multi-layer platform that may include a software layer (e.g., software  1318 ) of diagnostic applications (or other application types) that may perform one or more medical imaging and diagnostic functions. In at least one embodiment, system  1400  may be communicatively coupled to (e.g., via encrypted links) PACS server networks of one or more facilities. In at least one embodiment, system  1400  may be configured to access and referenced data from PACS servers to perform operations, such as training machine learning models, deploying machine learning models, image processing, inferencing, and/or other operations. 
     In at least one embodiment, a software layer may be implemented as a secure, encrypted, and/or authenticated API through which applications or containers may be invoked (e.g., called) from an external environment(s) (e.g., facility  1302 ). In at least one embodiment, applications may then call or execute one or more services  1320  for performing compute, AI, or visualization tasks associated with respective applications, and software  1318  and/or services  1320  may leverage hardware  1322  to perform processing tasks in an effective and efficient manner. 
     In at least one embodiment, deployment system  1306  may execute deployment pipelines  1410 . In at least one embodiment, deployment pipelines  1410  may include any number of applications that may be sequentially, non-sequentially, or otherwise applied to imaging data (and/or other data types) generated by imaging devices, sequencing devices, genomics devices, etc.—including AI-assisted annotation, as described above. In at least one embodiment, as described herein, a deployment pipeline  1410  for an individual device may be referred to as a virtual instrument for a device (e.g., a virtual ultrasound instrument, a virtual CT scan instrument, a virtual sequencing instrument, etc.). In at least one embodiment, for a single device, there may be more than one deployment pipeline  1410  depending on information desired from data generated by a device. In at least one embodiment, where detections of anomalies are desired from an Mill machine, there may be a first deployment pipeline  1410 , and where image enhancement is desired from output of an Mill machine, there may be a second deployment pipeline  1410 . 
     In at least one embodiment, an image generation application may include a processing task that includes use of a machine learning model. In at least one embodiment, a user may desire to use their own machine learning model, or to select a machine learning model from model registry  1324 . In at least one embodiment, a user may implement their own machine learning model or select a machine learning model for inclusion in an application for performing a processing task. In at least one embodiment, applications may be selectable and customizable, and by defining constructs of applications, deployment and implementation of applications for a particular user are presented as a more seamless user experience. In at least one embodiment, by leveraging other features of system  1400 —such as services  1320  and hardware  132 —deployment pipelines  1410  may be even more user friendly, provide for easier integration, and produce more accurate, efficient, and timely results. 
     In at least one embodiment, deployment system  1306  may include a user interface  1414  (e.g., a graphical user interface, a web interface, etc.) that may be used to select applications for inclusion in deployment pipeline(s)  1410 , arrange applications, modify or change applications or parameters or constructs thereof, use and interact with deployment pipeline(s)  1410  during set-up and/or deployment, and/or to otherwise interact with deployment system  1306 . In at least one embodiment, although not illustrated with respect to training system  1304 , user interface  1414  (or a different user interface) may be used for selecting models for use in deployment system  1306 , for selecting models for training, or retraining, in training system  1304 , and/or for otherwise interacting with training system  1304 . 
     In at least one embodiment, pipeline manager  1412  may be used, in addition to an application orchestration system  1428 , to manage interaction between applications or containers of deployment pipeline(s)  1410  and services  1320  and/or hardware  1322 . In at least one embodiment, pipeline manager  1412  may be configured to facilitate interactions from application to application, from application to service  1320 , and/or from application or service to hardware  1322 . In at least one embodiment, although illustrated as included in software  1318 , this is not intended to be limiting, and in some examples (e.g., as illustrated in  FIG. 12   cc ) pipeline manager  1412  may be included in services  1320 . In at least one embodiment, application orchestration system  1428  (e.g., Kubernetes, DOCKER, etc.) may include a container orchestration system that may group applications into containers as logical units for coordination, management, scaling, and deployment. In at least one embodiment, by associating applications from deployment pipeline(s)  1410  (e.g., a reconstruction application, a segmentation application, etc.) with individual containers, each application may execute in a self-contained environment (e.g., at a kernel level) to increase speed and efficiency. 
     In at least one embodiment, each application and/or container (or image thereof) may be individually developed, modified, and deployed (e.g., a first user or developer may develop, modify, and deploy a first application and a second user or developer may develop, modify, and deploy a second application separate from a first user or developer), which may allow for focus on, and attention to, a task of a single application and/or container(s) without being hindered by tasks of another application(s) or container(s). In at least one embodiment, communication, and cooperation between different containers or applications may be aided by pipeline manager  1412  and application orchestration system  1428 . In at least one embodiment, so long as an expected input and/or output of each container or application is known by a system (e.g., based on constructs of applications or containers), application orchestration system  1428  and/or pipeline manager  1412  may facilitate communication among and between, and sharing of resources among and between, each of applications or containers. In at least one embodiment, because one or more of applications or containers in deployment pipeline(s)  1410  may share same services and resources, application orchestration system  1428  may orchestrate, load balance, and determine sharing of services or resources between and among various applications or containers. In at least one embodiment, a scheduler may be used to track resource requirements of applications or containers, current usage or planned usage of these resources, and resource availability. In at least one embodiment, a scheduler may thus allocate resources to different applications and distribute resources between and among applications in view of requirements and availability of a system. In some examples, a scheduler (and/or other component of application orchestration system  1428 ) may determine resource availability and distribution based on constraints imposed on a system (e.g., user constraints), such as quality of service (QoS), urgency of need for data outputs (e.g., to determine whether to execute real-time processing or delayed processing), etc. 
     In at least one embodiment, services  1320  leveraged by and shared by applications or containers in deployment system  1306  may include compute services  1416 , AI services  1418 , visualization services  1420 , and/or other service types. In at least one embodiment, applications may call (e.g., execute) one or more of services  1320  to perform processing operations for an application. In at least one embodiment, compute services  1416  may be leveraged by applications to perform super-computing or other high-performance computing (HPC) tasks. In at least one embodiment, compute service(s)  1416  may be leveraged to perform parallel processing (e.g., using a parallel computing platform  1430 ) for processing data through one or more of applications and/or one or more tasks of a single application, substantially simultaneously. In at least one embodiment, parallel computing platform  1430  (e.g., NVIDIA&#39;s CUDA) may enable general purpose computing on GPUs (GPGPU) (e.g., GPUs  1422 ). In at least one embodiment, a software layer of parallel computing platform  1430  may provide access to virtual instruction sets and parallel computational elements of GPUs, for execution of compute kernels. In at least one embodiment, parallel computing platform  1430  may include memory and, in some embodiments, a memory may be shared between and among multiple containers, and/or between and among different processing tasks within a single container. In at least one embodiment, inter-process communication (IPC) calls may be generated for multiple containers and/or for multiple processes within a container to use same data from a shared segment of memory of parallel computing platform  1430  (e.g., where multiple different stages of an application or multiple applications are processing same information). In at least one embodiment, rather than making a copy of data and moving data to different locations in memory (e.g., a read/write operation), same data in same location of a memory may be used for any number of processing tasks (e.g., at a same time, at different times, etc.). In at least one embodiment, as data is used to generate new data as a result of processing, this information of a new location of data may be stored and shared between various applications. In at least one embodiment, location of data and a location of updated or modified data may be part of a definition of how a payload is understood within containers. 
     In at least one embodiment, AI services  1418  may be leveraged to perform inferencing services for executing machine learning model(s) associated with applications (e.g., tasked with performing one or more processing tasks of an application). In at least one embodiment, AI services  1418  may leverage AI system  1424  to execute machine learning model(s) (e.g., neural networks, such as CNNs) for segmentation, reconstruction, object detection, feature detection, classification, and/or other inferencing tasks. In at least one embodiment, applications of deployment pipeline(s)  1410  may use one or more of output models  1316  from training system  1304  and/or other models of applications to perform inference on imaging data. In at least one embodiment, two or more examples of inferencing using application orchestration system  1428  (e.g., a scheduler) may be available. In at least one embodiment, a first category may include a high priority/low latency path that may achieve higher service level agreements, such as for performing inference on urgent requests during an emergency, or for a radiologist during diagnosis. In at least one embodiment, a second category may include a standard priority path that may be used for requests that may be non-urgent or where analysis may be performed at a later time. In at least one embodiment, application orchestration system  1428  may distribute resources (e.g., services  1320  and/or hardware  1322 ) based on priority paths for different inferencing tasks of AI services  1418 . 
     In at least one embodiment, shared storage may be mounted to AI services  1418  within system  1400 . In at least one embodiment, shared storage may operate as a cache (or other storage device type) and may be used to process inference requests from applications. In at least one embodiment, when an inference request is submitted, a request may be received by a set of API instances of deployment system  1306 , and one or more instances may be selected (e.g., for best fit, for load balancing, etc.) to process a request. In at least one embodiment, to process a request, a request may be entered into a database, a machine learning model may be located from model registry  1324  if not already in a cache, a validation step may ensure appropriate machine learning model is loaded into a cache (e.g., shared storage), and/or a copy of a model may be saved to a cache. In at least one embodiment, a scheduler (e.g., of pipeline manager  1412 ) may be used to launch an application that is referenced in a request if an application is not already running or if there are not enough instances of an application. In at least one embodiment, if an inference server is not already launched to execute a model, an inference server may be launched. Any number of inference servers may be launched per model. In at least one embodiment, in a pull model, in which inference servers are clustered, models may be cached whenever load balancing is advantageous. In at least one embodiment, inference servers may be statically loaded in corresponding, distributed servers. 
     In at least one embodiment, inferencing may be performed using an inference server that runs in a container. In at least one embodiment, an instance of an inference server may be associated with a model (and optionally a plurality of versions of a model). In at least one embodiment, if an instance of an inference server does not exist when a request to perform inference on a model is received, a new instance may be loaded. In at least one embodiment, when starting an inference server, a model may be passed to an inference server such that a same container may be used to serve different models so long as inference server is running as a different instance. 
     In at least one embodiment, during application execution, an inference request for a given application may be received, and a container (e.g., hosting an instance of an inference server) may be loaded (if not already), and a start procedure may be called. In at least one embodiment, pre-processing logic in a container may load, decode, and/or perform any additional pre-processing on incoming data (e.g., using a CPU(s) and/or GPU(s)). In at least one embodiment, once data is prepared for inference, a container may perform inference as necessary on data. In at least one embodiment, this may include a single inference call on one image (e.g., a hand X-ray), or may require inference on hundreds of images (e.g., a chest CT). In at least one embodiment, an application may summarize results before completing, which may include, without limitation, a single confidence score, pixel level-segmentation, voxel-level segmentation, generating a visualization, or generating text to summarize findings. In at least one embodiment, different models or applications may be assigned different priorities. For example, some models may have a real-time (TAT&lt;1 min) priority while others may have lower priority (e.g., TAT&lt;10 min). In at least one embodiment, model execution times may be measured from requesting institution or entity and may include partner network traversal time, as well as execution on an inference service. 
     In at least one embodiment, transfer of requests between services  1320  and inference applications may be hidden behind a software development kit (SDK), and robust transport may be provide through a queue. In at least one embodiment, a request will be placed in a queue via an API for an individual application/tenant ID combination and an SDK will pull a request from a queue and give a request to an application. In at least one embodiment, a name of a queue may be provided in an environment from where an SDK will pick it up. In at least one embodiment, asynchronous communication through a queue may be useful as it may allow any instance of an application to pick up work as it becomes available. Results may be transferred back through a queue, to ensure no data is lost. In at least one embodiment, queues may also provide an ability to segment work, as highest priority work may go to a queue with most instances of an application connected to it, while lowest priority work may go to a queue with a single instance connected to it that processes tasks in an order received. In at least one embodiment, an application may run on a GPU-accelerated instance generated in cloud  1426 , and an inference service may perform inferencing on a GPU. 
     In at least one embodiment, visualization services  1420  may be leveraged to generate visualizations for viewing outputs of applications and/or deployment pipeline(s)  1410 . In at least one embodiment, GPUs  1422  may be leveraged by visualization services  1420  to generate visualizations. In at least one embodiment, rendering effects, such as ray-tracing, may be implemented by visualization services  1420  to generate higher quality visualizations. In at least one embodiment, visualizations may include, without limitation, 2D image renderings, 3D volume renderings, 3D volume reconstruction, 2D tomographic slices, virtual reality displays, augmented reality displays, etc. In at least one embodiment, virtualized environments may be used to generate a virtual interactive display or environment (e.g., a virtual environment) for interaction by users of a system (e.g., doctors, nurses, radiologists, etc.). In at least one embodiment, visualization services  1420  may include an internal visualizer, cinematics, and/or other rendering or image processing capabilities or functionality (e.g., ray tracing, rasterization, internal optics, etc.). 
     In at least one embodiment, hardware  1322  may include GPUs  1422 , AI system  1424 , cloud  1426 , and/or any other hardware used for executing training system  1304  and/or deployment system  1306 . In at least one embodiment, GPUs  1422  (e.g., NVIDIA&#39;s TESLA and/or QUADRO GPUs) may include any number of GPUs that may be used for executing processing tasks of compute services  1416 , AI services  1418 , visualization services  1420 , other services, and/or any of features or functionality of software  1318 . For example, with respect to AI services  1418 , GPUs  1422  may be used to perform pre-processing on imaging data (or other data types used by machine learning models), post-processing on outputs of machine learning models, and/or to perform inferencing (e.g., to execute machine learning models). In at least one embodiment, cloud  1426 , AI system  1424 , and/or other components of system  1400  may use GPUs  1422 . In at least one embodiment, cloud  1426  may include a GPU-optimized platform for deep learning tasks. In at least one embodiment, AI system  1424  may use GPUs, and cloud  1426 —or at least a portion tasked with deep learning or inferencing—may be executed using one or more AI systems  1424 . As such, although hardware  1322  is illustrated as discrete components, this is not intended to be limiting, and any components of hardware  1322  may be combined with, or leveraged by, any other components of hardware  1322 . 
     In at least one embodiment, AI system  1424  may include a purpose-built computing system (e.g., a super-computer or an HPC) configured for inferencing, deep learning, machine learning, and/or other artificial intelligence tasks. In at least one embodiment, AI system  1424  (e.g., NVIDIA&#39;s DGX) may include GPU-optimized software (e.g., a software stack) that may be executed using a plurality of GPUs  1422 , in addition to CPUs, RAM, storage, and/or other components, features, or functionality. In at least one embodiment, one or more AI systems  1424  may be implemented in cloud  1426  (e.g., in a data center) for performing some or all of AI-based processing tasks of system  1400 . 
     In at least one embodiment, cloud  1426  may include a GPU-accelerated infrastructure (e.g., NVIDIA&#39;s NGC) that may provide a GPU-optimized platform for executing processing tasks of system  1400 . In at least one embodiment, cloud  1426  may include an AI system(s)  1424  for performing one or more of AI-based tasks of system  1400  (e.g., as a hardware abstraction and scaling platform). In at least one embodiment, cloud  1426  may integrate with application orchestration system  1428  leveraging multiple GPUs to enable seamless scaling and load balancing between and among applications and services  1320 . In at least one embodiment, cloud  1426  may tasked with executing at least some of services  1320  of system  1400 , including compute services  1416 , AI services  1418 , and/or visualization services  1420 , as described herein. In at least one embodiment, cloud  1426  may perform small and large batch inference (e.g., executing NVIDIA&#39;s TENSOR RT), provide an accelerated parallel computing API and platform  1430  (e.g., NVIDIA&#39;s CUDA), execute application orchestration system  1428  (e.g., KUBERNETES), provide a graphics rendering API and platform (e.g., for ray-tracing, 2D graphics, 3D graphics, and/or other rendering techniques to produce higher quality cinematics), and/or may provide other functionality for system  1400 . 
       FIG. 15A  illustrates a data flow diagram for a process  1500  to train, retrain, or update a machine learning model, in accordance with at least one embodiment. In at least one embodiment, process  1500  may be executed using, as a non-limiting example, system  1400  of  FIG. 14 . In at least one embodiment, process  1500  may leverage services  1320  and/or hardware  1322  of system  1400 , as described herein. In at least one embodiment, refined models  1512  generated by process  1500  may be executed by deployment system  1306  for one or more containerized applications in deployment pipelines  1410 . 
     In at least one embodiment, model training  1314  may include retraining or updating an initial model  1504  (e.g., a pre-trained model) using new training data (e.g., new input data, such as customer dataset  1506 , and/or new ground truth data associated with input data). In at least one embodiment, to retrain, or update, initial model  1504 , output or loss layer(s) of initial model  1504  may be reset, or deleted, and/or replaced with an updated or new output or loss layer(s). In at least one embodiment, initial model  1504  may have previously fine-tuned parameters (e.g., weights and/or biases) that remain from prior training, so training or retraining  1314  may not take as long or require as much processing as training a model from scratch. In at least one embodiment, during model training  1314 , by having reset or replaced output or loss layer(s) of initial model  1504 , parameters may be updated and re-tuned for a new data set based on loss calculations associated with accuracy of output or loss layer(s) at generating predictions on new, customer dataset  1506  (e.g., image data  1308  of  FIG. 13 ). 
     In at least one embodiment, pre-trained models  1406  may be stored in a data store, or registry (e.g., model registry  1324  of  FIG. 13 ). In at least one embodiment, pre-trained models  1406  may have been trained, at least in part, at one or more facilities other than a facility executing process  1500 . In at least one embodiment, to protect privacy and rights of patients, subjects, or clients of different facilities, pre-trained models  1406  may have been trained, on-premise, using customer or patient data generated on-premise. In at least one embodiment, pre-trained models  1406  may be trained using cloud  1426  and/or other hardware  1322 , but confidential, privacy protected patient data may not be transferred to, used by, or accessible to any components of cloud  1426  (or other off premise hardware). In at least one embodiment, where a pre-trained model  1406  is trained at using patient data from more than one facility, pre-trained model  1406  may have been individually trained for each facility prior to being trained on patient or customer data from another facility. In at least one embodiment, such as where a customer or patient data has been released of privacy concerns (e.g., by waiver, for experimental use, etc.), or where a customer or patient data is included in a public data set, a customer or patient data from any number of facilities may be used to train pre-trained model  1406  on-premise and/or off premise, such as in a datacenter or other cloud computing infrastructure. 
     In at least one embodiment, when selecting applications for use in deployment pipelines  1410 , a user may also select machine learning models to be used for specific applications. In at least one embodiment, a user may not have a model for use, so a user may select a pre-trained model  1406  to use with an application. In at least one embodiment, pre-trained model  1406  may not be optimized for generating accurate results on customer dataset  1506  of a facility of a user (e.g., based on patient diversity, demographics, types of medical imaging devices used, etc.). In at least one embodiment, prior to deploying pre-trained model  1406  into deployment pipeline  1410  for use with an application(s), pre-trained model  1406  may be updated, retrained, and/or fine-tuned for use at a respective facility. 
     In at least one embodiment, a user may select pre-trained model  1406  that is to be updated, retrained, and/or fine-tuned, and pre-trained model  1406  may be referred to as initial model  1504  for training system  1304  within process  1500 . In at least one embodiment, customer dataset  1506  (e.g., imaging data, genomics data, sequencing data, or other data types generated by devices at a facility) may be used to perform model training  1314  (which may include, without limitation, transfer learning) on initial model  1504  to generate refined model  1512 . In at least one embodiment, ground truth data corresponding to customer dataset  1506  may be generated by training system  1304 . In at least one embodiment, ground truth data may be generated, at least in part, by clinicians, scientists, doctors, practitioners, at a facility (e.g., as labeled clinic data  1312  of  FIG. 13 ). 
     In at least one embodiment, AI-assisted annotation  1310  may be used in some examples to generate ground truth data. In at least one embodiment, AI-assisted annotation  1310  (e.g., implemented using an AI-assisted annotation SDK) may leverage machine learning models (e.g., neural networks) to generate suggested or predicted ground truth data for a customer dataset. In at least one embodiment, user  1510  may use annotation tools within a user interface (a graphical user interface (GUI)) on computing device  1508 . 
     In at least one embodiment, user  1510  may interact with a GUI via computing device  1508  to edit or fine-tune (auto)annotations. In at least one embodiment, a polygon editing feature may be used to move vertices of a polygon to more accurate or fine-tuned locations. 
     In at least one embodiment, once customer dataset  1506  has associated ground truth data, ground truth data (e.g., from AI-assisted annotation, manual labeling, etc.) may be used by during model training  1314  to generate refined model  1512 . In at least one embodiment, customer dataset  1506  may be applied to initial model  1504  any number of times, and ground truth data may be used to update parameters of initial model  1504  until an acceptable level of accuracy is attained for refined model  1512 . In at least one embodiment, once refined model  1512  is generated, refined model  1512  may be deployed within one or more deployment pipelines  1410  at a facility for performing one or more processing tasks with respect to medical imaging data. 
     In at least one embodiment, refined model  1512  may be uploaded to pre-trained models  1406  in model registry  1324  to be selected by another facility. In at least one embodiment, his process may be completed at any number of facilities such that refined model  1512  may be further refined on new datasets any number of times to generate a more universal model. 
       FIG. 15B  is an example illustration of a client-server architecture  1532  to enhance annotation tools with pre-trained annotation models, in accordance with at least one embodiment. In at least one embodiment, AI-assisted annotation tools  1536  may be instantiated based on a client-server architecture  1532 . In at least one embodiment, annotation tools  1536  in imaging applications may aid radiologists, for example, identify organs and abnormalities. In at least one embodiment, imaging applications may include software tools that help user  1510  to identify, as a non-limiting example, a few extreme points on a particular organ of interest in raw images  1534  (e.g., in a 3D MRI or CT scan) and receive auto-annotated results for all 2D slices of a particular organ. In at least one embodiment, results may be stored in a data store as training data  1538  and used as (for example and without limitation) ground truth data for training. In at least one embodiment, when computing device  1508  sends extreme points for AI-assisted annotation  1310 , a deep learning model, for example, may receive this data as input and return inference results of a segmented organ or abnormality. In at least one embodiment, pre-instantiated annotation tools, such as AI-Assisted Annotation Tool  1536 B in  FIG. 15B , may be enhanced by making API calls (e.g., API Call  1544 ) to a server, such as an Annotation Assistant Server  1540  that may include a set of pre-trained models  1542  stored in an annotation model registry, for example. In at least one embodiment, an annotation model registry may store pre-trained models  1542  (e.g., machine learning models, such as deep learning models) that are pre-trained to perform AI-assisted annotation on a particular organ or abnormality. These models may be further updated by using training pipelines  1404 . In at least one embodiment, pre-installed annotation tools may be improved over time as new labeled clinic data  1312  is added. 
     Such components can be used to predict motion for generating animation. This can include using a motion prediction network trained as discussed herein to generate individual frames of animation. 
     Other variations are within spirit of present disclosure. Thus, while disclosed techniques are susceptible to various modifications and alternative constructions, certain illustrated embodiments thereof are shown in drawings and have been described above in detail. It should be understood, however, that there is no intention to limit disclosure to specific form or forms disclosed, but on contrary, intention is to cover all modifications, alternative constructions, and equivalents falling within spirit and scope of disclosure, as defined in appended claims. 
     Use of terms “a” and “an” and “the” and similar referents in context of describing disclosed embodiments (especially in context of following claims) are to be construed to cover both singular and plural, unless otherwise indicated herein or clearly contradicted by context, and not as a definition of a term. Terms “comprising,” “having,” “including,” and “containing” are to be construed as open-ended terms (meaning “including, but not limited to,”) unless otherwise noted. Term “connected,” when unmodified and referring to physical connections, is to be construed as partly or wholly contained within, attached to, or joined together, even if there is something intervening. Recitation of ranges of values herein are merely intended to serve as a shorthand method of referring individually to each separate value falling within range, unless otherwise indicated herein and each separate value is incorporated into specification as if it were individually recited herein. Use of term “set” (e.g., “a set of items”) or “subset,” unless otherwise noted or contradicted by context, is to be construed as a nonempty collection comprising one or more members. Further, unless otherwise noted or contradicted by context, term “subset” of a corresponding set does not necessarily denote a proper subset of corresponding set, but subset and corresponding set may be equal. 
     Conjunctive language, such as phrases of form “at least one of A, B, and C,” or “at least one of A, B and C,” unless specifically stated otherwise or otherwise clearly contradicted by context, is otherwise understood with context as used in general to present that an item, term, etc., may be either A or B or C, or any nonempty subset of set of A and B and C. For instance, in illustrative example of a set having three members, conjunctive phrases “at least one of A, B, and C” and “at least one of A, B and C” refer to any of following sets: {A}, {B}, {C}, {A, B}, {A, C}, {B, C}, {A, B, C}. Thus, such conjunctive language is not generally intended to imply that certain embodiments require at least one of A, at least one of B, and at least one of C each to be present. In addition, unless otherwise noted or contradicted by context, term “plurality” indicates a state of being plural (e.g., “a plurality of items” indicates multiple items). A plurality is at least two items, but can be more when so indicated either explicitly or by context. Further, unless stated otherwise or otherwise clear from context, phrase “based on” means “based at least in part on” and not “based solely on.” 
     Operations of processes described herein can be performed in any suitable order unless otherwise indicated herein or otherwise clearly contradicted by context. In at least one embodiment, a process such as those processes described herein (or variations and/or combinations thereof) is performed under control of one or more computer systems configured with executable instructions and is implemented as code (e.g., executable instructions, one or more computer programs or one or more applications) executing collectively on one or more processors, by hardware or combinations thereof. In at least one embodiment, code is stored on a computer-readable storage medium, for example, in form of a computer program comprising a plurality of instructions executable by one or more processors. In at least one embodiment, a computer-readable storage medium is a non-transitory computer-readable storage medium that excludes transitory signals (e.g., a propagating transient electric or electromagnetic transmission) but includes non-transitory data storage circuitry (e.g., buffers, cache, and queues) within transceivers of transitory signals. In at least one embodiment, code (e.g., executable code or source code) is stored on a set of one or more non-transitory computer-readable storage media having stored thereon executable instructions (or other memory to store executable instructions) that, when executed (i.e., as a result of being executed) by one or more processors of a computer system, cause computer system to perform operations described herein. A set of non-transitory computer-readable storage media, in at least one embodiment, comprises multiple non-transitory computer-readable storage media and one or more of individual non-transitory storage media of multiple non-transitory computer-readable storage media lack all of code while multiple non-transitory computer-readable storage media collectively store all of code. In at least one embodiment, executable instructions are executed such that different instructions are executed by different processors—for example, a non-transitory computer-readable storage medium store instructions and a main central processing unit (“CPU”) executes some of instructions while a graphics processing unit (“GPU”) executes other instructions. In at least one embodiment, different components of a computer system have separate processors and different processors execute different subsets of instructions. 
     Accordingly, in at least one embodiment, computer systems are configured to implement one or more services that singly or collectively perform operations of processes described herein and such computer systems are configured with applicable hardware and/or software that enable performance of operations. Further, a computer system that implements at least one embodiment of present disclosure is a single device and, in another embodiment, is a distributed computer system comprising multiple devices that operate differently such that distributed computer system performs operations described herein and such that a single device does not perform all operations. 
     Use of any and all examples, or exemplary language (e.g., “such as”) provided herein, is intended merely to better illuminate embodiments of disclosure and does not pose a limitation on scope of disclosure unless otherwise claimed. No language in specification should be construed as indicating any non-claimed element as essential to practice of disclosure. 
     All references, including publications, patent applications, and patents, cited herein are hereby incorporated by reference to same extent as if each reference were individually and specifically indicated to be incorporated by reference and were set forth in its entirety herein. 
     In description and claims, terms “coupled” and “connected,” along with their derivatives, may be used. It should be understood that these terms may be not intended as synonyms for each other. Rather, in particular examples, “connected” or “coupled” may be used to indicate that two or more elements are in direct or indirect physical or electrical contact with each other. “Coupled” may also mean that two or more elements are not in direct contact with each other, but yet still co-operate or interact with each other. 
     Unless specifically stated otherwise, it may be appreciated that throughout specification terms such as “processing,” “computing,” “calculating,” “determining,” or like, refer to action and/or processes of a computer or computing system, or similar electronic computing device, that manipulate and/or transform data represented as physical, such as electronic, quantities within computing system&#39;s registers and/or memories into other data similarly represented as physical quantities within computing system&#39;s memories, registers or other such information storage, transmission or display devices. 
     In a similar manner, term “processor” may refer to any device or portion of a device that processes electronic data from registers and/or memory and transform that electronic data into other electronic data that may be stored in registers and/or memory. As non-limiting examples, “processor” may be a CPU or a GPU. A “computing platform” may comprise one or more processors. As used herein, “software” processes may include, for example, software and/or hardware entities that perform work over time, such as tasks, threads, and intelligent agents. Also, each process may refer to multiple processes, for carrying out instructions in sequence or in parallel, continuously or intermittently. Terms “system” and “method” are used herein interchangeably insofar as system may embody one or more methods and methods may be considered a system. 
     In present document, references may be made to obtaining, acquiring, receiving, or inputting analog or digital data into a subsystem, computer system, or computer-implemented machine. Obtaining, acquiring, receiving, or inputting analog and digital data can be accomplished in a variety of ways such as by receiving data as a parameter of a function call or a call to an application programming interface. In some implementations, process of obtaining, acquiring, receiving, or inputting analog or digital data can be accomplished by transferring data via a serial or parallel interface. In another implementation, process of obtaining, acquiring, receiving, or inputting analog or digital data can be accomplished by transferring data via a computer network from providing entity to acquiring entity. References may also be made to providing, outputting, transmitting, sending, or presenting analog or digital data. In various examples, process of providing, outputting, transmitting, sending, or presenting analog or digital data can be accomplished by transferring data as an input or output parameter of a function call, a parameter of an application programming interface or interprocess communication mechanism. 
     Although discussion above sets forth example implementations of described techniques, other architectures may be used to implement described functionality, and are intended to be within scope of this disclosure. Furthermore, although specific distributions of responsibilities are defined above for purposes of discussion, various functions and responsibilities might be distributed and divided in different ways, depending on circumstances. 
     Furthermore, although subject matter has been described in language specific to structural features and/or methodological acts, it is to be understood that subject matter claimed in appended claims is not necessarily limited to specific features or acts described. Rather, specific features and acts are disclosed as exemplary forms of implementing the claims.