Patent Publication Number: US-2023134690-A1

Title: Neural rendering for inverse graphics generation

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This application is a continuation of U.S. patent application Ser. No. 17/193,405, filed Mar. 5, 2021, and entitled “Neural Rendering For Inverse Graphics Generation,” which claims priority to U.S. Provisional Patent Application Ser. No. 62/986,618, filed Mar. 6, 2020, and entitled “3D Neural Rendering and Inverse Graphics with StyleGAN Renderer,” which are hereby incorporated herein in their entirety and for all purposes. 
     This application is also related to co-pending U.S. patent application Ser. No. 17/019,120, filed Sep. 11, 2020, and entitled “Labeling Images Using a Neural Network,” as well as co-pending U.S. patent application Ser. No. 17/020,649, filed Sep. 14, 2020, and entitled “Generating Labels for Synthetic Images Using One or More Neural Networks,” each of which is hereby incorporated herein in its entirety and for all purposes. 
    
    
     BACKGROUND 
     A variety of different industries rely upon three-dimensional (3D) modeling for various purposes, including those that require the generation of representations of 3D environments. In order to provide realistic complex environments, it is necessary to have a variety of different types of objects, or similar objects with different appearances, to avoid unrealistic repetition or omissions. Unfortunately, obtaining a large number of three dimensional models can be a complex, expensive, and time (and resource) intensive process. It may be desirable to generate 3D environments from a large amount of available two-dimensional (2D) data, but many existing approaches do not provide for adequate 3D model generation based on 2D data. For approaches that prove promising, such as may involve machine learning, it is still necessary to have a sufficiently large number and variety of labeled training data in order to train the machine learning. An insufficient number and variety of annotated training data instances can prevent a model from being sufficiently trained to produce acceptably accurate results. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       Various embodiments in accordance with the present disclosure will be described with reference to the drawings, in which: 
         FIGS.  1 A,  1 B, and  1 C  illustrate image data that can be utilized, according to at least one embodiment; 
         FIG.  2    illustrates a neural rendering and inverse graphics pipeline, according to at least one embodiment; 
         FIG.  3    illustrates components of a an image generation system, according to at least one embodiment; 
         FIG.  4    illustrates representative points used to determine view or pose information for an object, according to at least one embodiment; 
         FIG.  5    illustrates a process for training an inverse graphics network, according to at least one embodiment; 
         FIG.  6    illustrates components of a system for training and utilizing an inverse graphics network, according to at least one embodiment; 
         FIG.  7 A  illustrates inference and/or training logic, according to at least one embodiment; 
         FIG.  7 B  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.  15 A and  15 B  illustrate a data flow diagram for a process to train a machine learning model, as well as client-server architecture to enhance annotation tools with pre-trained annotation models, in accordance with at least one embodiment. 
     
    
    
     DETAILED DESCRIPTION 
     Approaches in accordance with various embodiments can provide for the generation of three-dimensional (3D) models, or at least the inferencing of one or more 3D properties (e.g., shape, texture, or light), from two-dimensional (2D) input, such as images or video frames. In particular, various embodiments provide approaches to train an inverse graphics model to generate accurate 3D models or representations from 2D images. In at least some embodiments, a generative model can be used to generate multiple views of an object from different viewpoints or with different poses, with other image features or aspects being kept fixed. The generated images can include pose, view, or camera information that was used to generate each such image. These images can be used as training images for an inverse graphics network. The inverse graphics network can utilize such a set of input images for a single object to generate and refine a model for that object. In at least some embodiments, these networks can be trained together, where a 3D model output by the inverse graphics network can be fed as input training data to the generator network for purposes of improving the accuracy of the generator network. A combined loss function can be used with terms for both the inverse graphics network and the generator network, in order to optimize both networks together using a common set of training data. Such an approach can provide for improved 3D reconstruction performance with respect to prior approaches, can provide for category generalization (i.e., a model does not need to be trained for only a specific type of class of object), and can significantly reduce a need for manual annotation (e.g., from many hours to a minute or less). 
     In at least one embodiment, an inverse graphics network can be trained to generate a 3D model or representation, or otherwise determine 3D information, from a 2D image of an object, such as the input image  100  of  FIG.  1 A  that illustrates a front view of a vehicle. As can be appreciated, the input image provides at least three challenges for reconstructing a 3D model or representation of that vehicle. A first challenge is that there is only image data for the front of the vehicle in this example, so image data for other portions of the vehicle (e.g., the back, underneath, or sides) would need to be completely inferred and generated. For example, other view images  140  are illustrated in  FIG.  1 B  which show different views of the sides of the vehicle, where portions of the side of the vehicle are represented that were not included in the original input image. Another challenge is that there is no depth information in the 2D image. Any depth or shape information must be inferred based only on the two-dimensional single view. Yet another challenge is that there is no camera or pose information or annotation provided with the input image  100 , to use as a reference when instructed to generate other views or portions of a 3D model. 
     In order to train a model, network, or algorithm (e.g., an inverse graphics model) to generate or infer such 3D information, including semantic information, approaches in accordance with various embodiments can provide view images  140  such as those illustrated in  FIG.  1 B  as training images for a network. As mentioned, however, it can be difficult to obtain a sufficient number of views for a given object, and even if a sufficient number is obtained, those images must be annotated with sufficient information, such as pose, camera, or view data, to enable those images to function as ground truth training data. 
     Approaches in accordance with various embodiments can utilize a generator that is able to take an input image, such as image  100  of  FIG.  1 A , and generate a set of images of that object in different views. In at least one embodiment, this can be a generative neural network, such as a generative adversarial network (GAN). A GAN can be provided with an input image as well as pose, view, or camera data, and can generate an image representation of an object in the input image having, or corresponding to, the provided pose, view, or camera data. In order to generate a set of training image for this object, a set of pose, view, or camera information can be provided (manually, randomly, or according to a specified pattern or rule) and an image can be generated for each unique pose, view, or camera input. Another advantage of such a process is that the GAN can provide the annotations without any additional processing, as the pose, view, or camera information was already known and used in generating the images. An example GAN that can be used for such a purpose is a style generative adversarial network, also referred to as StyleGAN, developed by NVIDIA Corporation. According to one or more embodiments, a StyleGAN model extends the general GAN architecture to include a mapping network to map points in latent space to an intermediate latent space, which can control the “style” (e.g., pose, view, or camera information) at each point in the generator model, and can also introduce noise as a source of variation at each of these points. In one or more embodiments, a StyleGAN model can be used to generate large, accurate sets of multi-view data in short time and with relatively little processing resources required. A StyleGAN model can take an input image  140  and generate multiple different view images  140  for use in training an inverse graphics network. The inverse graphics network can then be trained to take 2D input images and generate or infer relatively accurate 3D information  180  as illustrated in  FIG.  1 C , such as may relate to the shape of an object (as may be represented by a 3D mesh), the lighting of the object (e.g., direction, intensity, and color of one or more light sources), and the texture of the object (e.g., a complete set of image data representing all relevant portions of the object. As known for applications such as computer graphics, a view of an object can be generated by projecting the texture onto the mesh, lighting the mesh using the appropriate lighting information, then rendering an image of that object from a determined point of view of a virtual camera. Other sets or types of 3D information may be used as well for different use cases, applications, or embodiments. The 3D information generated by the inverse graphics network can then, in turn, be used as training data for the StyleGAN model. These networks can be trained separately using separate loss functions or together using a common loss function in various embodiments. 
       FIG.  2    illustrates an example training pipeline  200  that can be utilized in accordance with various embodiments. This example pipeline  200  includes two different renderers. The first renderer is a generator network, such as a GAN (e.g., StyleGAN), and the second renderer in this example is a differentiable graphics renderer, such as an interpolation-based differentiable renderer (DIB-R). A DIB-R is a differentiable rendering framework that allows gradients to be analytically computed for all pixels in an image. This framework can view foreground rasterization as a weighted interpolation of local properties and background rasterization as a distance-based aggregation of global geometry, allowing for accurate optimization over vertex positions, colors, normals, light directions, and texture coordinates through a variety of lighting models. In this example, the generator  204  is used as a synthetic data generator with efficient annotation of a multi-view dataset  206 . This dataset can then be used to train an inverse graphics network  212  that predicts 3D properties from the 2D images. This network can be used to disentangle the latent code of the generator through a carefully designed mapping network. 
     In this example, input images of the multi-view dataset can be provided as input to the inverse graphics network  208 , which can utilize an inferencing network to infer 3D information such as the shape, lighting, and texture of an object in the image. For training purposes, this 3D information can be fed as input, along with the camera information from the input image, to a differentiable graphics renderer. This renderer can utilize the 3D information to generate shape information, a 3D model, or one or more images for given camera information. These renderings can then be compared against the relevant ground truth data, using an appropriate loss function, to determine the loss values. The loss can then be used to adjust one or more network weights or parameters. As mentioned, the output of the inverse graphics network  208  can also be used to further train or fine tune the generator  204 , or StyleGAN, to generate more accurate images. 
       FIG.  3    illustrates a portion  300  of such a pipeline that can be utilized in accordance with at least one embodiment. As mentioned with respect to  FIG.  2   , 3D information (or “codes”) about an object in an image can be inferred, as may include a mesh  304 , texture  306 , and background information  308 . Camera information can also be utilized that was extracted from one or more annotations of the input image. In this example the mesh  304  is processed using a machine learning processor (MLLP) to extract one or more dimensions or latent features. The texture  306  and background data  308  can be processed with respective encoders  314 ,  316 , such as one or more convolutional neural networks (CNNs), to also extract the relevant dimensions or features. These features or dimensions can then be passed to a disentangling module  318  that includes a mapping network. The mapping network will attempt to map the features of the various codes into a single latent code  320  that be provided to the generator  322 , or renderer, for generating an output image  322 . In at least one embodiment, a portion of a latent code will correspond to the camera information, and the rest will correspond to the mesh, texture, and background. An attempt can be made to merge the features into a single set of features, instead of three sets of features for the mesh, texture, and background, before generating the latent code  320 . A selection matrix S can be used to select and merge these features, along with the camera features, into the latent code  320 . This information can then be provided to the generator, such as a StyleGAN, for rendering an image. The latent code may also take other forms, such as a latent space or feature vector of a determined dimension, such as for around 500 features. The entry for each dimension can contain information about the image, but it may be unknown which feature or dimension controls or contains which information or aspect of the image. In order to generate different image views of the same object, the features corresponding to the camera (which are known—say the first 100 features) can be varied while leaving the other features fixed. Such an approach can ensure that only the view or pose changes while other aspects of the object or image remain unchanged between rendered images. 
     In at least some embodiments, there may need to be at least some type of camera or object view or pose information provided as annotations.  FIG.  4    illustrates one example approach to providing annotation points  402  for a type of object in a set  400  of image views. Instead of labeling all key points for an object, which can take a lot of effort, weak camera information can be utilized which only includes a subset of potential features. In this example, the camera pose can be split into a number of different ranges, such as twelve ranges. Given these feature points and the pose ranges, this can be sufficient to obtain a rough estimate of the object position for use in annotating the image for use as training data. The process can then initialize from this weakly accurate camera pose information. Such a process can work with multiple types of objects as well, such as people, animals, vehicles, and so on. 
     Approaches in accordance with various embodiments can thus utilize differentiable rendering to assist in the training of one or more neural networks to perform inverse graphics-related tasks, as may include (without limitation) tasks such as predicting 3D geometry from monocular (e.g., 2D) photographs. To train high performing models, many conventional approaches rely on multi-view imagery which is not readily available in practice. Recent Generative Adversarial Networks (GANs) that synthesize images, in contrast, seem to acquire 3D knowledge implicitly during training: object viewpoints can be manipulated by simply manipulating the latent codes. However, these latent codes often lack further physical interpretation and thus GANs cannot easily be inverted to perform explicit 3D reasoning. 3D knowledge learned by generative models can be extracted and disentangled, at least in part, by utilizing differentiable renderers. In at least one embodiment, one or more generative adversarial networks (GANs) can be exploited as a multi-view data generator to train an inverse graphics network. This can be performed using an off-the-shelf differentiable renderer, and the trained inverse graphics network as a teacher to disentangle the latent code of the GAN into interpretable 3D properties. In various approaches, an entire architecture can be trained iteratively using cycle consistency losses. Such an approach can provide significantly improved performance with respect to conventional inverse graphics networks trained on existing datasets, both quantitatively and via user studies. Such a disentangled GAN can also be utilized as a controllable 3D “neural renderer,” which can be used to complement traditional graphics renderers. 
     An ability to infer 3D properties such as geometry, texture, material, and light from photographs is key in many domains such as AR/VR, robotics, architecture, and computer vision. Interest in this problem has been explosive, particularly in the past few years, as evidenced by a large body of published works and several released 3D libraries. The process of going from images to 3D is often called “inverse graphics,” since the problem is inverse to the process of rendering in graphics in which a 3D scene is projected onto a 2D image by considering the geometry and material properties of objects, and light sources present in the scene. Most work on inverse graphics assumes that 3D labels are available during training, and trains a neural network to predict these labels. To ensure high quality 3D ground-truth, synthetic datasets such as ShapeNet are typically used. However, models trained on synthetic datasets often struggle on real photographs due to the domain gap with synthetic imagery. 
     To circumvent at least some of these issues, an alternative approach to training an inverse graphics networks can be used that sidesteps the need for 3D ground-truth during training. Graphics renderers can be made differentiable, which allows one to infer 3D properties directly from images using gradient based optimization. At least some of these approaches can employ a neural network to predict geometry, texture, and light from images by minimizing the difference between the input image and the image rendered from these properties. While impressive results have been obtained in some approaches, many of these approaches still require some form of implicit 3D supervision, such as may utilize multi-view images of the same object with known cameras. On the other hand, generative models of images appear to learn 3D information implicitly, where manipulating the latent code can produce images of the same scene from a different viewpoint. However, the learned latent space typically lacks physical interpretation and is usually not disentangled, where properties such as the 3D shape and color of the object often cannot be manipulated independently. 
     Approaches in accordance with at least one embodiment can extract and disentangle 3D knowledge learned by generative models by utilizing differentiable graphics renderers. In at least one embodiment, a generator, such as a GAN, can be exploited as a generator of multi-view imagery to train an inverse graphics neural network using a differentiable renderer. In turn, the inverse graphics network can be used to inform the generator about the image formation process through the knowledge from graphics, effectively disentangling the latent space of the GAN. In at least one embodiment, a GAN (e.g., StyleGAN) can be connected with an inverse graphics network to form a single architecture that can be iteratively trained using cycle-consistency losses. Such an approach can produce a trained network that can significantly outperform inverse graphics networks on existing datasets, and can provide for controllable 3D generation and manipulation of imagery using a disentangled generative model. 
     A pipeline that can be used for such an approach was described previously with respect to  FIG.  2   . Such an approach can combine two types of renderers: a GAN-based neural “renderer” and a differentiable graphics renderer. In at least one embodiment, such an approach can leverage the fact that a GAN can learn to produce highly-realistic images of objects, and allows for a reliable control over the virtual camera used to generate views of those objects. A set of camera views can be selected, manually or otherwise, such with rough viewpoint annotation. A GAN, such as StyleGAN, can then be used to generate a large number of examples per view. Such a dataset can be used to train an inverse graphics network utilizing a differentiable renderer, such as DIB-R. A trained inverse graphics network can be used to disentangle the latent code of the GAN, and turn the GAN into a 3D neural renderer, allowing for control over explicit 3D properties. 
     In at least one embodiment, a generator such as a StyleGAN model can be used to generate multi-view imagery. An example StyleGAN model is a 16 layer neural network that maps a latent code z E Z drawn from a normal distribution into a realistic image. The code z is first mapped to an intermediate latent code w E W which is transformed to w*=(w 1 *, w 2 , . . . w 16 *)∈W* through 16 learned affine transformations. W* is referred to as the transformed latent space to differentiate it from the intermediate latent space W. Transformed latent codes w* are then injected as the style information to a StyleGAN Synthesis network. 
     Different layers can control different image attributes in the generator. Styles in early layers adjust the camera viewpoint while styles in the intermediate and higher layers influence shape, texture, and background. It was empirically determined that the latent code w v *:=(w 1* , w 2* , w 3* , w 4 *) in the first four layers controls camera viewpoints in at least one StyleGAN model. That is, if a process samples a new code w v * but keeps the remaining dimensions of w* fixed (which is referred to as the content code), images of the same object depicted in a different viewpoint can be generated. It can further be observed that a sampled code w* in fact represents a fixed camera viewpoint. That is, if w v * is kept fixed but the remaining dimensions of w* sampled, the generator can produce imagery of different objects in the same camera viewpoint. The objects in each of the viewpoints will be aligned, as this generator is functioning as a multi-view data generator. 
     In an example approach, several views can be manually selected that cover all the common viewpoints of an object ranging from 0-360 degrees in azimuth and roughly 0-30 degrees in elevation. Such an approach may pay attention to choose viewpoints in which the objects appear most consistent. Since inverse graphics techniques generally utilize camera pose information, the chosen viewpoint codes can be annotated with a rough absolute camera pose. To be specific, each viewpoint code may be classified into, for example, one of twelve azimuth angles, uniformly sampled along 360 degree increments. Each code can be assigned code a fixed elevation (e.g., 0°) and camera distance. These camera poses can provide a very coarse annotation of the actual pose, as the annotation serves as the initialization of the camera which will be optimized during training. Such an approach provides for annotation of all views, and thus the entire dataset, in a relatively short period of time, such as one minute or less. Such a result can make the annotation effort effectively negligible. For each viewpoint, a large number of content codes can be sampled to synthesize different objects in these views. Since a differentiable renderer, such as DIB-R, can also utilize segmentation masks during training, a network such as MaskRCNN can be further applied to obtain instance segmentation in the generated dataset. As a generator may sometimes generate unrealistic images or images with multiple objects, images which have more than one instance, or small masks (less than 10% of the whole image area), can be filtered out in at least one embodiment. 
     Approaches in accordance with at least one embodiment can aim to train a 3D prediction network f, parameterized by θ, to infer 3D shapes (as may be represented as meshes) along with textures from images. Let I v  denote an image in viewpoint V from a generator dataset, and M its corresponding object mask. The inverse graphics network makes a prediction as follows: {S, T}f θ =(I V ) where S denotes the predicted shape, and T a texture map. Shape S is deformed from a sphere. While DIB-R also supports prediction of lighting, its performance may not be sufficient for realistic imagery, so lighting estimation is omitted in the present discussion. 
     To train the network, a renderer such as DIB-R can be adopted as the differentiable graphics renderer that takes {S, T} and V as input and produces a rendered image I V ′=r(S, T, V) along with a rendered mask M′. Following DIB-R, the loss function then takes the following form: 
         L ( I,S,T,V ;θ)=λ col   L   col ( I,I ′)+λ percept   L   percept ( I,I ′)+ L   IOU ( M,M ′)+λ sm   L   sm ( S )+λ lap   L   lap ( S )+λ mov   L   mov ( S )
 
     Here, L col  is the standard L 1  image reconstruction loss defined in the RGB color space while L percept  is the perceptual loss that helps the predicted texture look more realistic. Note that rendered images do not have background, so L col  and L percept  are calculated by utilizing the mask. L IOU  computes the intersection-over-union between the ground-truth mask and the rendered mask. Regularization losses such as the Laplacian loss L lap  and flatten loss L sm  are commonly used to ensure that the shape is well behaved. Finally, L mov  regularizes the shape deformation to be uniform and small. 
     Since there may also be access to multi-view images for each object, a multi-view consistency loss can be included. In particular, the loss per object k can be given by: 
     
       
         
           
             
               
                 
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     While more views provide more constraints, empirically, two views have been proven sufficient. View pairs (i,j) can be randomly sampled for efficiency. The above loss functions can be used to jointly train the neural network f and optimize viewpoint cameras V. It may be assumed that different images generated from the same w* correspond to the same viewpoint V. Optimizing the camera jointly with the weights of the network allows such an approach to effectively deal with noisy initial camera annotations. 
     The inverse graphics model allows for the inferring of a 3D mesh and texture from a given image. These 3D properties can then be used to disentangle the latent space of the generator, and turn the generator into a fully controllable 3D neural renderer, such as may be referred to as StyleGAN-R. It can be noted that StyleGAN in fact synthesizes more than just an object, it also produces the background to result in an entire scene. Approaches in at least some embodiments can also provide control over the background as well, allowing the neural renderer to render 3D objects into desired scenes. To get the background from a given image, the object can be masked out in at least one embodiment. 
     A mapping network can be trained and used to map the viewpoint, shape (e.g., mesh), texture, and background into the latent code of the generator. Since the generator may not be completely disentangled, the entire generative model can be fine-tuned while keeping the inverse graphics network fixed. A mapping network, such as the example illustrated in  FIG.  3   , can map the viewpoints to the first four layers and map the shape, texture, and background to the last twelve layers of W*. For simplicity, the first four layers can be denoted as WV*, and the last twelve layers as WS*TB, where WV*∈R2048 and WS*TB∈R3008. It should be noted that there may be different numbers of layers in other models or networks. In this example, the mapping network gv for viewpoint V and gs for shape S are separate MLPs while gt for texture T and gb for background B are CNN layers: 
         z   view   =g   v ( V;θ   v ), z   shape   =g   s ( S;θ   s ), z   txt   =g   t ( T;θ   t ), z   bck   =g   b ( B;θ   b ), 
     where z view ∈R, z shape , z txt , z bck ∈R 3008  and θ v , θ s , θ t , θ b  are network parameters. The shape, texture, and background codes can be softly combined into the final latent code as follows: 
         {tilde over (w)}   mtb   =s   m   ⊙z   shape   +z   txt   +s   b   ⊙z   bck , 
     where ⊙ denotes element-wise product, and s m , s t , s b ∈R 3008  are shared across all the samples. To achieve disentanglement, each dimension of the final code can be explained by only one property (e.g., shape, texture, or background). A process in accordance with at least one embodiment can thus normalize each dimension of s using a SoftMax. In practice, it was determined that mapping V to a high dimensional code may be challenging since a dataset may only contain a limited number of views, and V may be limited to azimuth, elevation, and scale. One approach is to map V to the subset of W V *, where the approach empirically chooses a number, such as 144 of the 2048, or dimensions with the highest correlation with the annotated viewpoints. Thus, z view ∈R 144 ∈R144 in this example. 
     In at least one embodiment, the mapping network can be trained and the StyleGAN model fine-tuned in two separate stages. In one example, the StyleGAN model&#39;s weights are frozen and only the mapping network is trained. In one or more embodiments, this facilitates or improves the ability of the mapping network to output reasonable latent codes for the StyleGAN model. The process can then fine-tune both the StyleGAN model and the mapping network to better disentangle different attributes. In a warmup stage, viewpoint codes w* can be sampled among the chosen viewpoints, and the remaining dimensions of w*∈W* sampled. An attempt can be made to minimize the L 2  difference between the mapped code w and StyleGAN model&#39;s code w*. To encourage the disentanglement in the latent space, the entropy of each dimension i of s can be penalized. An example overall loss function for this mapping network can then be given by: 
     
       
         
           
             
               
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     By training the mapping network, that view, shape and texture can be disentangled in the original StyleGAN model, but the background may remain entangled. The model can thus be fine-tuned to achieve a better disentanglement. A cycle consistency loss can be incorporated in order to fine-tune the StyleGAN model. In particular, by feeding a sampled shape, texture and background to the StyleGAN model a synthesized image can be obtained. Such an approach can encourage consistency between the original sampled properties and the shape, texture, and background predicted from the StyleGAN-synthesized image via the inverse graphics network. The same background B can be fed with two different {S, T} pairs to generate two images I 1  and I 2 . The re-synthesized backgrounds B 1  and B 2  can then be encouraged to be similar. This loss tries to disentangle the background from the foreground object. During training, imposing the consistency loss on B in image space may result in blurry images, thus it can be constrained in the code space. An example fine-tuning loss takes the following form: 
         L   stylegan (θ gan   =∥S−{tilde over (S)}∥   2   +∥T− T ∥   2   +∥g   b ( B )− g   b (   B   ) 2   +∥g   b (   B     1 )− g   b     B     2 ∥ 2 )
 
     In one example, a DIB-R based inverse graphics model was trained with Adam, with a learning rate of 1e 4 , setting λ IOU , λ col , λ lap , λ sm  and λ mov  to 3, 20, 5, 5, and 2.5, respectively. The model was first trained with L col  loss for 3K iterations, and then fine-tuned by adding L precept  to make the texture more realistic. The process set A percept to 0.5. The model converged in 200K iterations with batch size 16. Training took around 120 hours on four V100 GPUs. The training resulted in high quality 3D reconstruction results, including quality of the predicted shapes and textures, and the diversity of the 3D shapes obtained. This method also worked well on more challenging (e.g., articulated) classes, such as animals. 
     In at least one embodiment, a StyleGAN-R model was trained using Adam with a learning rate of 1e 5  and batch size 16. Warmup stage took 700 iterations, and joint fine-tuning performed for another 2500 iterations. With the provided input image, the process first predicted mesh and texture using the trained inverse graphics model, and then fed these 3D properties into the StyleGAN-R to generate a new image. For comparison, the same 3D properties were fed to the DIB-R graphics renderer (which is the OpenGL renderer). It can be noted that DIB-R can only render the predicted object, while StyleGAN-R also has the ability to render the object into a desired background. It was found that StyleGAN-R produces relatively consistent images compared to the input image. Shape and texture are well preserved, while only the background has a slight content shift. 
     Such an approach was tested in manipulating StyleGAN-synthesized images from test set and real images. Specifically, given an input image, the approach predicted 3D properties using the inverse graphics network, and extracted background by masking out the object with a Mask-RCNN. The approach then manipulated and fed these properties to a StyleGAN-R to synthesize new views. 
     In order to control viewpoint, the process first froze shape, texture, and background, and changed only the camera viewpoint. Meaningful results were obtained, particularly for shape and texture. For comparison, an alternative way that has been explored is to directly optimize the GAN&#39;s latent code (in an example case the original StyleGAN&#39;s code) via an L2 image reconstruction loss. Such an approach may fail to generate plausible images, however, showcasing importance of the mapping network and fine-tuning the entire architecture with 3D inverse graphics network in the loop in at least some embodiments. 
     To control shape, texture, and background, such an approach can attempt to manipulate these or other 3D properties, while keeping the camera viewpoint fixed. In one example, the shapes of all cars to can be changed to one chosen shape and perform neural rendering performed using a StyleGAN-R. Such a process successfully swapped the shape of the car while maintaining other properties. The process is also able to modify tiny parts of the car, such as the trunk and headlights. The same experiment can be performed, but swapping texture and background. Swapping textures may also slightly modify the background in some embodiments, pointing that further improvements may be pursued in disentangling the two. Such a framework can also work well when provided with real images, since StyleGAN&#39;s images are quite realistic. 
     The StyleGAN code repository provides models of different object categories at different resolutions. Here a 512×384 car model is taken as an example. This model contains 16 layers, where every two consecutive layers form a block. Each block has a different number of channels. In the last block, the model produces a 32-channel feature map at a 512×384 resolution. Finally, a learned RGB transformation function is applied to convert the feature map into an RGB image. The feature map for each block can be visualized via the learned RGB transformation function. Specifically, for the feature map in each block with the size of h×w×c, the process can first sum along the feature dimension, forming a h×w×1 tensor. The process can repeat the feature 32 times and generate a h×w×32 new feature map. This allows keeping the information of all the channels and directly applying the RGB transformation function in the last block to convert it to the RGB image. In this example, blocks 1 and 2 do not exhibit interpretable structure while the car shape starts to appear in blocks 3-5. There is a rough car contour in block 4 which further becomes clear in block 5. From blocks 6 to 8, the car&#39;s shape becomes increasingly finer and background scene also appears. This supports that the viewpoint is controlled in block 1 and 2 (e.g., the first 4 layers) while shape, texture, and background exist in the last 12 layers in this example. There was high consistency of both the car shape and texture as well as the background scene across the different viewpoints. Note that for articulated objects such as horse and bird classes, a StyleGAN model may not perfectly preserve object articulation in different viewpoints, which may lead to challenges in training high accuracy models using multi-view consistency loss. 
     As mentioned, inverse graphics tasks require camera pose information during training, which can be challenging to acquire for real imagery. Pose is generally obtained by annotating key points for each object and running structure-from-motion (SFM) techniques to compute camera parameters. However, key point annotation is quite time consuming—requiring roughly 3-5 minutes per object in one experiment. A StyleGAN model can be utilized to significantly reduce annotation effort since samples with the same w v * share the same viewpoint. Therefore, the process only needs to assign a few selected w v * into camera poses. In particular, poses can be assigned into several bins which is sufficient for training inverse graphics networks where, along with the network parameters, cameras get jointly optimized during training using these bins as initialization. In one example, each view is annotated with a rough absolute camera pose (which can be further optimized during training). To be specific, one example can first select 12 azimuth angles: [0°, 30°, 60°, 90°, 120°, 150°, 180°, 210°, 240°, 270°, 300°, 330°]. Given a StyleGAN viewpoint, the process can include manually classifying which azimuth angle it is close to and assigning it to the corresponding label with fixed elevation (0°) and camera distance. 
     To demonstrate the effectiveness of this camera initialization, a comparison can be made with another inverse graphics network trained with a more accurate camera initialization. Such an initialization is done by manually annotating object key points in each of the selected views (w v *) of a single car example, which takes about 3-4 hours (around 200 minutes, 39 views). Note that this is still a significantly lower annotation effort compared to 200-350 hours required to annotate key points for every single object in the Pascal3D dataset. The camera parameters can then be computed using Structure from Motion (SfM). Reference can be made to the two inverse graphics networks trained with different camera initializations as view-model and key point-model, respectively. While it takes the same amount of time to train, view-model can save on annotation time. The performance of view-model and key point-model are comparable with almost the same 2D IOU re-projection score on the StyleGAN test set. Moreover, during training the two camera systems converge to the same position. This can be evaluated by converting all the views into quaternions and compare the difference between the rotation axes and rotation angles. Among all views, the average difference of the rotation axes is only 1.430 and the rotation angle is 0.420. The maximum difference of the rotation axes is only 2.950 and the rotation angle is 1.110. Both qualitative and quantitative comparisons demonstrated that view-camera initialization is sufficient for training accurate inverse graphics networks and no additional annotation is required. This demonstrates a scalable way for creating multi-view datasets with StyleGAN, with roughly a minute of annotation time per class. 
       FIG.  5    illustrates an example process  500  for training an inverse graphics network that can be utilized in accordance with various embodiments. It should be understood that for this and other processes presented herein that there can be additional, fewer, or alternative steps performed in similar or alternative order, or at least partially in parallel, within scope of various embodiments unless otherwise specifically stated. Further, although discussed with respect to streaming video content, it should be understood that such enhancements can be provided to individual images or image sequences, stored video files, augmented or virtual reality streams, or other such content. In this example, a two-dimensional (2D) training image is received that includes a representation of an object. A set of camera poses is also received  504 , or otherwise determined. Using the 2D image information with a generator network, such as a StyleGAN, a set of view images is generated including representations of the object with views according to the set of camera poses. Here, each generated image would include, or be associated with, the corresponding camera pose information. 
     In this example, the set of generated view images can then be provided  508  as input to an inverse graphics network. A set of three-dimensional (3D) information, such as may include shape, lighting, and texture, is inferred  510  for the object in the image. One or more representations of the object can then be rendered  512  using the 3D information and the camera information from the input image with a differentiable renderer. The rendered representation(s) can then be compared  514  against the corresponding ground truth data to determine one or more loss values. One or more network parameters or weights can then be adjusted  516  to attempt to minimize the loss. A determination can be made  518  as to whether an end condition has been satisfied, such as the network converging, a maximum number of training passes being reached, or all training data being processed, among other such options. If not, then the process can continue with the next 2D training image. If an end criterion has been satisfied, then the optimized network parameters can be provided  520  for use in inferencing. At least some of the rendered output from the inverse graphics network can also be provided  524  as training data for further training, or fine-tuning, the generator network. In some embodiments, the generator network and inverse graphics network can be trained together using a common loss function. 
     As an example,  FIG.  6    illustrates a network configuration  600  that can be used to provide or enhance content. In at least one embodiment, a client device  602  can generate content for a session using components of a content application  604  on client device  602  and data stored locally on that client device. In at least one embodiment, a content application  624  (e.g., an image generation or editing application) executing on content server  620  (e.g., a cloud server or edge server) may initiate a session associated with at least client device  602 , as may utilize a session manager and user data stored in a user database  634 , and can cause content  632  to be determined by a content manager  626  and rendered using a rendering engine, if needed for this type of content or platform, and transmitted to client device  602  using an appropriate transmission manager  622  to send by download, streaming, or another such transmission channel. In at least one embodiment, this content  632  can include 2D or 3D assets that can be used by a rendering engine to render a scene based on a determined scene graph. In at least one embodiment, client device  602  receiving this content can provide this content to a corresponding content application  604 , which may also or alternatively include a rendering engine (if necessary) for rendering at least some of this content for presentation via client device  602 , such as image or video content through a display  606  and audio, such as sounds and music, through at least one audio playback device  608 , such as speakers or headphones. For live video content captured by one or more cameras, for example, such a rendering engine may not be needed, unless used to augment that video content in some way. In at least one embodiment, at least some of this content may already be stored on, rendered on, or accessible to client device  602  such that transmission over network  640  is not required for at least that portion of content, such as where that content may have been previously downloaded or stored locally on a hard drive or optical disk. In at least one embodiment, a transmission mechanism such as data streaming can be used to transfer this content from server  620 , or content database  634 , to client device  602 . In at least one embodiment, at least a portion of this content can be obtained or streamed from another source, such as a third party content service  660  that may also include a content application  662  for generating or providing content. In at least one embodiment, portions of this functionality can be performed using multiple computing devices, or multiple processors within one or more computing devices, such as may include a combination of CPUs and GPUs. 
     In at least one embodiment, content application  624  includes a content manager  626  that can determine or analyze content before this content is transmitted to client device  602 . In at least one embodiment, content manager  626  can also include, or work with, other components that are able to generate, modify, or enhance content to be provided. In at least one embodiment, this can include a rendering engine for rendering image or video content. This rendering engine may be part of an inverse graphics network in at least some embodiments. In at least one embodiment, an image, video, or scene generation component  628  can be used to generate image, video, or other media content. In at least one embodiment, an inverse graphics component  630 , which can also include a neural network, can generate representations based on inferred 3D information, as discussed and suggested herein. In at least one embodiment, content manager  626  can cause this content (enhanced or not) to be transmitted to client device  602 . In at least one embodiment, a content application  604  on client device  602  may also include components such as a rendering engine, image or video generator  612 , and inverse graphics module  614 , such that any or all of this functionality can additionally, or alternatively, be performed on client device  602 . In at least one embodiment, a content application  662  on a third party content service system  660  can also include such functionality. In at least one embodiment, locations where at least some of this functionality is performed may be configurable, or may depend upon factors such as a type of client device  602  or availability of a network connection with appropriate bandwidth, among other such factors. In at least one embodiment, a system for content generation can include any appropriate combination of hardware and software in one or more locations. In at least one embodiment, generated image or video content of one or more resolutions can also be provided, or made available, to other client devices  650 , such as for download or streaming from a media source storing a copy of that image or video content. In at least one embodiment, this may include transmitting images of game content for a multiplayer game, where different client devices may display that content at different resolutions, including one or more super-resolutions. 
     In this example, these client devices can include any appropriate computing devices, as may include a desktop computer, notebook computer, set-top box, streaming device, gaming console, smartphone, tablet computer, VR headset, AR goggles, wearable computer, or a smart television. Each client device can submit a request across at least one wired or wireless network, as may include the Internet, an Ethernet, a local area network (LAN), or a cellular network, among other such options. In this example, these requests can be submitted to an address associated with a cloud provider, who may operate or control one or more electronic resources in a cloud provider environment, such as may include a data center or server farm. In at least one embodiment, the request may be received or processed by at least one edge server, that sits on a network edge and is outside at least one security layer associated with the cloud provider environment. In this way, latency can be reduced by enabling the client devices to interact with servers that are in closer proximity, while also improving security of resources in the cloud provider environment. 
     In at least one embodiment, such a system can be used for performing graphical rendering operations. In other embodiments, such a system can be used for other purposes, such as for providing image or video content to test or validate autonomous machine applications, or for performing deep learning operations. In at least one embodiment, such a system can be implemented using an edge device, or may incorporate one or more Virtual Machines (VMs). In at least one embodiment, such a system can be implemented at least partially in a data center or at least partially using cloud computing resources. 
     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.  7 A and/or  7 B . 
     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.  7 A and/or  7 B . 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 train an inverse graphics network using a set of images generated by a generator network, where aspects of objects are kept fixed while pose or view information is varied between images of the set. 
     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.  7 A and/or  7 B . 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 train an inverse graphics network using a set of images generated by a generator network, where aspects of objects are kept fixed while pose or view information is varied between images of the set. 
       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 train an inverse graphics network using a set of images generated by a generator network, where aspects of objects are kept fixed while pose or view information is varied between images of the set. 
       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.  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 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  FIG.  7 A or  7 B . 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 train an inverse graphics network using a set of images generated by a generator network, where aspects of objects are kept fixed while pose or view information is varied between images of the set. 
       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  FIG.  7 A or  7 B . 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 train an inverse graphics network using a set of images generated by a generator network, where aspects of objects are kept fixed while pose or view information is varied between images of the set. 
     Virtualized Computing Platform 
       FIG.  13    is an example data flow diagram for a process  1300  of generating and deploying an image 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 represents 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, 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.  15 B . 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 MRI machine, there may be a first deployment pipeline  1410 , and where image enhancement is desired from output of an MRI 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  1322 —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.  15 A  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.  15 B  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.  15 B , 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 train an inverse graphics network using a set of images generated by a generator network, where aspects of objects are kept fixed while pose or view information is varied between images of the set. 
     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.