Systems and Methods for Training Multi-Class Object Classification Models with Partially Labeled Training Data

Systems and methods of the present disclosure are directed to a computer-implemented method for training a machine-learned multi-class object classification model with partially labeled training data. The method can include obtaining image data depicting objects and ground truth data comprising a subset of object class annotations respectively associated with a subset of object classes of a plurality of object classes. The method can include processing the image data with the machine-learned multi-class object classification model to obtain object classification data. The method can include evaluating a loss function that evaluates a multi-class classification loss and adjusting one or more parameters of the multi-class object classification model based on the loss function.

FIELD

The present disclosure relates generally to training machine-learned object classification models. More particularly, the present disclosure relates to training machine-learned multi-class object classification models to detect and recognize multiple classes of objects depicted in image data using partially labeled training data.

BACKGROUND

Training machine-learned multi-class object classification models to detect and recognize multiple classes of objects generally utilizes image training data that is labeled with ground truth labeled bounding boxes for one or more of the multiple classes. This training data is often not completely labelled. That is, an explicit label is not given for every class. Instead, some labels may be implicitly inferred. For example, regions of the image data that are not included in the labeled bounding boxes (e.g., unlabeled) are generally assumed to not include any objects belonging to these classes.

However, these unlabeled regions often include other objects that correspond to object classes the model is trained to detect. As an example, a model may be trained to recognize cats and a training image may include a cat in an unlabeled region. If a label for this unlabeled region is implicitly inferred (e.g., a cat does not exist in the region), the model can be trained incorrectly (e.g., trained to not recognize the presence of the cat).

As such, the generation of training data for object classification models generally requires exhaustive annotation of all classes depicted in images of the training dataset. However, this annotation of the classification training dataset can be prohibitively expensive and/or time consuming. Further, contemporary attempts to train models using image data with partially labeled classes (e.g., only labeling two of three classes, etc.) has generally lead to significant model quality degradation.

SUMMARY

One example aspect of the present disclosure is directed to a computing system for training a multi-class object classification model with partially labeled training data. The computing system can include one or more processors. The computing system can include a machine-learned multi-class object classification model configured to classify a plurality of object classes. The computing system can include one or more tangible, non-transitory computer readable media storing computer-readable instructions that when executed by the one or more processors cause the one or more processors to perform operations. The operations can include obtaining image data depicting one or more objects and ground truth data comprising a subset of object class annotations respectively associated with a subset of object classes of the plurality of object classes. The operations can include processing the image data with the machine-learned multi-class object classification model to obtain object classification data. The operations can include evaluating a loss function that evaluates a multi-class classification loss comprising a difference between the object classification data and the subset of object class annotations, wherein the loss function comprises a plurality of weighted loss signals respectively associated with the plurality of object classes, wherein the weight of each of the weighted loss signals is based at least in part on the inclusion of the object class associated with the respective loss signal within the subset of object classes. The operations can include adjusting one or more parameters of the machine-learned multi-class object classification model based at least in part on the loss function.

Another example aspect of the present disclosure is directed to a computer-implemented method for training a machine-learned multi-class object classification model with partially labeled training data. The method can include obtaining, by a computing system comprising one or more computing devices, image data depicting one or more objects and ground truth data comprising a subset of object class annotations respectively associated with a subset of object classes of a plurality of object classes. The method can include processing, by the computing system, the image data with the machine-learned multi-class object classification model to obtain object classification data. The method can include evaluating, by the computing system, a loss function that evaluates a multi-class classification loss comprising a difference between the object classification data and the subset of object class annotations, wherein the loss function comprises a plurality of weighted loss signals respectively associated with the plurality of object classes, wherein the weight of each of the weighted loss signals is based at least in part on the inclusion of the object class associated with the respective loss signal within the subset of object classes. The method can include adjusting, by the computing system, one or more parameters of the machine-learned multi-class object classification model based at least in part on the loss function.

Another example aspect of the present disclosure is directed to one or more tangible, non-transitory computer readable media storing computer-readable instructions that when executed by one or more processors cause the one or more processors to perform operations. The operations can include obtaining image data depicting one or more objects and ground truth data comprising a subset of object class annotations respectively associated with a subset of object classes of a plurality of object classes. The operations can include processing the image data with a machine-learned multi-class object classification model to obtain object classification data, wherein the machine-learned multi-class object classification model is configured to classify each of the one or more objects as belonging to an object class of the plurality of object classes. The operations can include modifying a loss function to obtain a modified loss function, wherein the loss function comprises a plurality of loss signals respectively associated with the plurality of object classes, wherein the modified loss function comprises a subset of the plurality of loss signals respectively associated with the subset of object classes. The operations can include evaluating the modified loss function, wherein the modified loss function evaluates a multi-class classification loss comprising a difference between the object classification data and the subset of object class annotations. The operations can include adjusting one or more parameters of the machine-learned multi-class object classification model based at least in part on the loss function.

DETAILED DESCRIPTION

Overview

Generally, the present disclosure is directed to training machine-learned multi-class object classification models using partially labeled training data. More particularly, the present disclosure relates to training machine-learned multi-class object classification models to recognize multiple classes of objects depicted in image data using partially labeled training data. As an example, a machine-learned multi-class object classification model can be configured to classify objects depicted in image data as belonging to a plurality of classes (e.g., a bear class, a lion class, a tiger class, a kangaroo class, etc.). Image data that depicts one or more objects can be obtained alongside ground truth data that includes a subset of object class annotations (e.g., ground truth class labels) associated with a subset of the plurality of object classes (e.g., two of four total classes, etc.). The image data can be processed with the machine-learned multi-class object classification model to obtain object classification data that classifies the object(s) depicted in the image data.

A loss function can be evaluated that evaluates a multi-class classification loss including a difference between the object classification data and the subset of object class annotations (e.g., a difference between an object classification and an associated label, etc.). More particularly, the loss function can include a plurality of weighted loss signals respectively associated with the plurality of object classes (e.g., a weighted bear loss signal for a bear class, a weighted lion loss signal for a lion class, etc.). The weight of each of the weighted loss signals can be based at least in part on the inclusion of the object class of the loss signal within the subset of object classes. For example, a weighted loss signal for a class from the plurality of object classes (e.g., a kangaroo class) that is not included in the subset of object classes (e.g., bear, lion, and tiger classes) can have a lower weight than a weighted loss signal for an object class (e.g., the bear class) that is included in the subset of object classes.

Parameter(s) of the multi-class object classification model can be adjusted based at least in part on the loss function (e.g., proportionally to the weights of the weighted loss signals). In such fashion, the machine-learned multi-class object classification model can be trained to recognize labeled classes without degrading model performance in regards to unlabeled classes. More particularly, by adjusting the relevance of or eliminating a loss signal for a class that is not labeled in the training data (e.g., not included in the subset of classes), the model can be trained such that an unlabeled region of a training image is treated with a less negative or neutral assumption as to the presence of an unlabeled class. By removing or reducing the impact of this assumption, the model can be trained with partially labeled training data without leading to model quality degradation for classification of unlabeled classes.

More particularly, image data that depicts one or more objects can be obtained alongside ground truth data. The ground truth data can include a subset of object class annotations (e.g., ground truth labels) that are respectively associated with a subset of object classes from a plurality of object classes. As an example, the image data can be or otherwise include one or more images (e.g., an image, a plurality of video frames, etc.) from a training dataset that is configured to be used to train a machine-learned classification model to recognize and classify a plurality of classes. The image from the training dataset can include four objects belonging to four separate classes. The subset of object class annotations can be or otherwise include annotations for the first two classes of the four separate classes. As another example, the ground truth data for a second image of the training dataset can include object class annotations for the remaining two classes of the four separate classes. In such fashion, the subset of class annotations included in the ground truth data can, in some implementations, include different classes from the plurality of classes for different images included in a training dataset.

In some implementations, the object class annotations can include a bounding box that defines a region of the image data and an associated class label. As an example, the image data may depict a lion object. The object class annotation can include a bounding box that defines the region of the image data that includes the lion and a corresponding “lion” label. Alternatively, or additionally, in some implementations, the object class annotation can label the entire image of the image data as including or not including a class. As an example, the image data may not depict a tiger. The object class annotation can indicate that a tiger is not depicted in the image data.

In some implementations, the image data can be a portion of image data from an image. As an example, the image data can be a portion of image data extracted from an image based on a prediction that the portion of image data depicts an object (e.g., extracted by a region proposal network (RPN), etc.). As another example, the image data can be a portion of image data that is not defined by a bounding box of an object class annotation. Alternatively, in some implementations, the image data can be or otherwise include the entirety of the depiction of the image data. As an example, the image data can be obtained and can subsequently be processed iteratively by the machine-learned multi-class object classification model to recognize regions that are predicted to include an object and subsequently or concurrently classify the object.

The image data can be processed with the machine-learned multi-class object classification model to obtain object classification data. The machine-learned multi-class object classification model can be or otherwise include any sort of conventional machine-learned object classification model. As an example, the machine-learned model can be or can otherwise include one or more neural networks (e.g., deep neural networks, recurrent neural networks, graph neural networks, etc.) or the like. Neural networks (e.g., deep neural networks) can be feed-forward neural networks, convolutional neural networks, and/or various other types of neural networks.

The machine-learned multi-class object classification model can be configured to classify a plurality of object classes. More particularly, the machine-learned multi-class object classification model can be configured to process image data to classify an object depicted in the image data as belonging to a class from the plurality of classes. Additionally, or alternatively, in some implementations the machine-learned multi-class object classification model can be a portion (e.g., a component of a model, one or more layers of a model, etc.) of a machine-learned image analysis model that performs both object recognition and object classification. As an example, the machine-learned multi-class object classification model may be an object classification portion of a conventional two-stage object detector model (e.g., Faster-RCNN, etc.). For example, a first stage of the two-stage object detector model (e.g., a region proposal network (RPN), etc.) may obtain the image data and output a region of the image data (e.g., a region not defined by a bounding box, etc.) predicted to contain an object (e.g., using an anchoring-based approach, etc.). The region of the image data can be processed with the machine-learned multi-class object classification model to obtain the object classification data.

As another example, the machine-learned multi-class object classification model can be or otherwise be incorporated in a one-stage object detector model (e.g., a 1-stage single shot detector model, etc.) For example, the machine-learned multi-class object classification model can be or otherwise include a plurality of machine-learned layers (e.g., convolutional layer(s), activation layer(s), etc.) that predict region(s) that may include a predicted object that can subsequently be processed with additional classification layers of the multi-class object classification model to generate the object classification data. For another example, the machine-learned multi-class object classification model can include a plurality of layers that predict a region of the image data to include a predicted object and simultaneously classify the predicted object. In such fashion, the method of training the machine-learned multi-class object classification model can be applied to any conventional machine-learned image analysis model (e.g., a single shot detector (SSD) model, a you-only-look-once (YOLO) model, a Faster-RCNN model, etc.).

The object classification data output by the machine-learned multi-class object classification model can provide a multi-class classification output for an object depicted in the image data. As an example, a portion of the image may depict a bear object. The machine-learned multi-class object classification model can be configured to classify an object as belonging to one of four classes (e.g., a bear, a tiger, a kangaroo, a lion, etc.). The object classification data may include a plurality of predicted object class annotations indicative of whether an object belongs to each of the classes (e.g., bear 1, tiger 0, kangaroo 0, lion 0, etc.). Alternatively, in some implementations, the object classification data may include a plurality of object class probability predictions that the object depicted in the image data belongs to each of the four classes (e.g., 80% bear, 15% tiger, 15% kangaroo, 10% lion, etc.). In some implementations, the object classification data may include a single indication that the object depicted in the image data belongs to a class. As an example, the object classification data may be or otherwise include a predicted class annotation (e.g., a “bear” class annotation, etc.). As such, the object classification data can include a plurality of predictive probability outputs for a respective plurality of classes and/or a predicted class annotation for an object depicted in the image data or a portion of the image data. The object classification data output by the machine-learned multi-class object classification model will be discussed in greater detail with regards toFIGS.4and5.

A loss function can be evaluated that evaluates a multi-class classification loss. The multi-class classification loss can include a difference between the object classification data and the subset of object class annotations (e.g., a difference between a predicted object class annotation and a ground truth object class annotation, etc.). More particularly, the loss function can include a plurality of weighted loss signals that are respectively associated with the plurality of object classes (e.g., a bear weighted loss signal for a bear class, a lion weighted loss signal for a lion, etc.). The weight of each of the weighted loss signals can be based at least in part on the inclusion of the object class associated with the respective loss signal within the subset of object classes.

As an example, a first weighted loss signal can be associated with a first class from the plurality of object classes that is not included in the subset of object classes. For example, the first class can be a kangaroo class and the subset of object classes can include a bear class, a lion class, and a tiger class. Since the first class is not included in the subset of classes, the weight of the first loss signal can be weighted as to reduce the impact of the first loss signal relative to the loss function. In some implementations, the weights of each of the weighted loss signals can be a normalized value. For example, the weight of the first loss signal can be a weight of 0 while the weight of a second loss signal associated with a class included in the subset of classes can be weighted 1 (e.g., to eliminate the loss signal from the loss function, etc.). For another example, the weight of the first loss signal can be a weight of 0.35 while the weight of a second loss signal associated with a class included in the subset of classes can be weighted 1 (e.g., to reduce the relevance of the first loss signal with respect to the second loss signal, etc.). In such fashion, the weights of the weighted loss signals can be configured to reduce or eliminate the impact of the respective loss signals that are not associated with classes included in the subset of classes (e.g., classes with associated object class annotations (labels), etc.).

One or more parameters of the machine-learned multi-class object classification model can be adjusted based at least in part on the loss function. More particularly, the parameter(s) of the machine-learned multi-class object classification model can be adjusted based on each of the weighted loss signals and their respective weights. In some implementations, the parameter(s) of the machine-learned multi-class object classification model can be adjusted proportionally to the weights of each of the weighted loss signals of the loss function. As an example, the loss function can be utilized to calculate a final loss value based on an evaluation of the difference between the object classification data and the ground truth data. The final loss value can be based proportionally on the weights of the respective weighted loss signals. For example, a weighted loss signal with a weight of zero may contribute nothing to calculation of the final loss value. The loss value can be back propagated through the machine-learned multi-class classification model, and one or more parameters of the model can be adjusted based on the final loss value (e.g., using a gradient descent algorithm, etc.). In such fashion, the adjustments to parameter(s) of the model can be proportional to the weights of the weighted loss signals, therefore reducing the impact of loss signals associated with unlabeled classes during training of the model.

In some implementations, evaluating the loss function can include evaluating a subset of weighted loss signals of the loss function that are respectively associated with the subset of object classes. More particularly, the loss function can include only the weighted loss signals that are associated with the object classes that are labeled (e.g., the subset of object class annotations of the ground truth data, etc.). In such fashion, the loss function can exclude any loss signal associated with an object class that is not annotated by the subset of object class annotations (e.g., is not labeled in the training data, etc.).

In some implementations, the machine-learned multi-class object classification model can be utilized after training. More particularly, the computing system can obtain additional image data depicting one or more additional objects. The image data can be processed using the machine-learned multi-class object classification model to obtain an image classification output. In some implementations, the image classification output can include one or more labels descriptive of the additional image data. As an example, the image classification output may include one or more object class annotations for the one or more additional objects. As another example, the image classification output may include one or more image annotations that annotate the entire image as belonging to one or more classes. For example, the machine-learned multi-class object classification model may label the one or more additional objects as being semantically related to a “nature” image (e.g., a rabbit object, a tree object, a boulder object, a grass object, etc.). Based on the object classification label(s), the image classification data may label the image as belonging to a “nature” image

It should be noted that the machine-learned multi-class object classification model can be utilized to detect the presence of specific classes of objects depicted in image data. More particularly, the machine-learned multi-class object classification model can be utilized for object detection by detecting an object that corresponds to a learned class of objects. As an example, the machine-learned object classification model can detect a bear object depicted in image data. As such, the machine-learned multi-class object classification model of the present embodiments can be a machine-learned object detection model and/or can be a component of a machine-learned object detection model.

The present disclosure provides a number of technical effects and benefits. As one example technical effect and benefit, the systems and methods of the present disclosure allow for the training of machine-learned models using partially labeled training data. As described previously, the process of labeling training data for a plurality of classes is an arduous task that is often considered prohibitively expensive in terms of both time and cost. Further, previous attempts to train machine-learned models using partially labeled training data has historically led to significant degradation in model performance. However, under the proposed approach, partially labeled training data can be utilized to train machine-learned models while suffering from little or no degradation in model performance. In turn, this advancement significantly reduces the computational, monetary, and manpower costs associated with labeling training data for machine-learned models. Additionally, this advancement allows for the repurposing of previously labeled training data for use in training multi-class object classification models.

Example Devices and Systems

FIG.1Adepicts a block diagram of an example computing system100that performs machine-learned multi-class object classification according to example embodiments of the present disclosure. The system100includes a user computing device102, a server computing system130, and a training computing system150that are communicatively coupled over a network180.

In some implementations, the user computing device102can store or include one or more machine-learned multi-class object classification models120. For example, the machine-learned multi-class object classification models120can be or can otherwise include various machine-learned models such as neural networks (e.g., deep neural networks) or other types of machine-learned models, including non-linear models and/or linear models. Neural networks can include feed-forward neural networks, recurrent neural networks (e.g., long short-term memory recurrent neural networks), convolutional neural networks or other forms of neural networks. Example machine-learned multi-class object classification models120are discussed with reference toFIGS.2-5.

In some implementations, the one or more machine-learned multi-class object classification models120can be received from the server computing system130over network180, stored in the user computing device memory114, and then used or otherwise implemented by the one or more processors112. In some implementations, the user computing device102can implement multiple parallel instances of a single machine-learned multi-class object classification model120(e.g., to perform parallel multi-class object classification across multiple instances of the machine-learned multi-class object classification model).

The machine-learned multi-class object classification model120can be configured to classify a plurality of objects. More particularly, the machine-learned multi-class object classification model120can be utilized to classify one or more objects depicted in image data as each belonging to one of a plurality of classes. As an example, the user computing device102can obtain additional image data (e.g., via network180, data116, etc.) depicting one or more additional objects. The image data can be processed using the machine-learned multi-class object classification model120to obtain an image classification output. In some implementations, the image classification output can include one or more labels descriptive of the additional image data. As an example, the image classification output may include a one or more object class annotations for the one or more additional objects. As another example, the image classification output may include one or more image annotations that annotate the entire image as belonging to one or more classes. For example, the machine-learned multi-class object classification model120may label the one or more additional objects as being semantically related to a “nature” image (e.g., a rabbit object, a tree object, a boulder object, a grass object, etc.). Based on the object classification label(s), the image classification data may label the image as belonging to a “nature” image class.

Additionally or alternatively, one or more machine-learned multi-class object classification models140can be included in or otherwise stored and implemented by the server computing system130that communicates with the user computing device102according to a client-server relationship. For example, the machine-learned multi-class object classification models140can be implemented by the server computing system140as a portion of a web service (e.g., a multi-class object classification service). Thus, one or more models120can be stored and implemented at the user computing device102and/or one or more models140can be stored and implemented at the server computing system130.

The user computing device102can also include one or more user input component122that receives user input. For example, the user input component122can be a touch-sensitive component (e.g., a touch-sensitive display screen or a touch pad) that is sensitive to the touch of a user input object (e.g., a finger or a stylus). The touch-sensitive component can serve to implement a virtual keyboard. Other example user input components include a microphone, a traditional keyboard, or other means by which a user can provide user input.

As described above, the server computing system130can store or otherwise include one or more machine-learned multi-class object classification models140. For example, the machine-learned multi-class object classification models140can be or can otherwise include various machine-learned models. Example machine-learned models include neural networks or other multi-layer non-linear models. Example neural networks include feed forward neural networks, deep neural networks, recurrent neural networks, and convolutional neural networks. Example models140are discussed with reference toFIGS.2-5.

The user computing device102and/or the server computing system130can train the models120and/or140via interaction with the training computing system150that is communicatively coupled over the network180. The training computing system150can be separate from the server computing system130or can be a portion of the server computing system130.

The training computing system150can include a model trainer160that trains the machine-learned models120and/or140stored at the user computing device102and/or the server computing system130using various training or learning techniques, such as, for example, backwards propagation of errors. For example, a loss function can be backpropagated through the model(s) to update one or more parameters of the model(s) (e.g., based on a gradient of the loss function). Various loss functions can be used such as mean squared error, likelihood loss, cross entropy loss, hinge loss, and/or various other loss functions. Gradient descent techniques can be used to iteratively update the parameters over a number of training iterations.

In particular, the model trainer160can train the models120and/or140based on a set of training data162. The training data162can include, for example, image data that depicts one or more objects can be obtained alongside ground truth data. The ground truth data can include a subset of object class annotations that are respectively associated with a subset of object classes from a plurality of object classes. As an example, the image data can be or otherwise include one or more images (e.g., an image, a plurality of video frames, etc.) from a training dataset that is configured to be used to train a machine-learned classification model to recognize and classify a plurality of classes. The image from the training dataset can include four objects belonging to four separate classes. The subset of object class annotations can be or otherwise include annotations for the first two classes of the four separate classes. As another example, the ground truth data for a second image of the training dataset can include object class annotations for the remaining two classes of the four separate classes. In such fashion, the ground truth data can, in some implementations, include different class annotations for different images included in a training dataset.

In some implementations, the object class annotations can include a bounding box that defines a region of the image data and an associated class label. As an example, the image data may depict a lion object. The object class annotation can include a bounding box that defines the region of the image data that includes the lion and a corresponding “lion” label. Alternatively, or additionally, in some implementations, the object class annotation can label the entire image of the image data as including or not including a class. As an example, the image data may not depict a tiger. The object class label can indicate that a tiger is not depicted in the image data.

In some implementations, the image data can be a portion of image data from an image. As an example, the image data can be a portion of image data extracted from an image based on a prediction that the portion of image data depicts an object (e.g., extracted by a region proposal network (RPN), etc.). As another example, the image data can be a portion of image data already defined by a bounding box of an object class annotation. Alternatively, in some implementations, the image data can be or otherwise include an entire image. As an example, the image can be obtained and can subsequently be processed iteratively by the machine-learned multi-class object classification model to recognize regions that are predicted to include an object and subsequently or concurrently classify the object.

In some implementations, if the user has provided consent, the training examples can be provided by the user computing device102. Thus, in such implementations, the model120provided to the user computing device102can be trained by the training computing system150on user-specific data received from the user computing device102. In some instances, this process can be referred to as personalizing the model.

The method of training of machine-learned models using partially labeled training data of the present embodiments can be utilized for a variety of machine-learned models, tasks, applications, and/or use cases.

In some implementations, the training data used to train the machine-learned model(s) of the present disclosure can be partially labeled image data. The machine-learned model(s) can process the image data to generate an output. As an example, the machine-learned model(s) can process the image data to generate an image recognition output (e.g., a recognition of the image data, a latent embedding of the image data, an encoded representation of the image data, a hash of the image data, etc.). As another example, the machine-learned model(s) can process the image data to generate an image segmentation output. As another example, the machine-learned model(s) can process the image data to generate an image classification output. As another example, the machine-learned model(s) can process the image data to generate an image data modification output (e.g., an alteration of the image data, etc.). As another example, the machine-learned model(s) can process the image data to generate an encoded image data output (e.g., an encoded and/or compressed representation of the image data, etc.). As another example, the machine-learned model(s) can process the image data to generate an upscaled image data output. As another example, the machine-learned model(s) can process the image data to generate a prediction output.

In some implementations, the training data used to train the machine-learned model(s) of the present disclosure can be partially labeled text or natural language data. The machine-learned model(s) can process the text or natural language data to generate an output. As an example, the machine-learned model(s) can process the natural language data to generate a language encoding output. As another example, the machine-learned model(s) can process the text or natural language data to generate a latent text embedding output. As another example, the machine-learned model(s) can process the text or natural language data to generate a translation output. As another example, the machine-learned model(s) can process the text or natural language data to generate a classification output. As another example, the machine-learned model(s) can process the text or natural language data to generate a textual segmentation output. As another example, the machine-learned model(s) can process the text or natural language data to generate a semantic intent output. As another example, the machine-learned model(s) can process the text or natural language data to generate an upscaled text or natural language output (e.g., text or natural language data that is higher quality than the input text or natural language, etc.). As another example, the machine-learned model(s) can process the text or natural language data to generate a prediction output.

In some implementations, the training data used to train the machine-learned model(s) of the present disclosure can be partially labeled speech data. The machine-learned model(s) can process the speech data to generate an output. As an example, the machine-learned model(s) can process the speech data to generate a speech recognition output. As another example, the machine-learned model(s) can process the speech data to generate a speech translation output. As another example, the machine-learned model(s) can process the speech data to generate a latent embedding output. As another example, the machine-learned model(s) can process the speech data to generate an encoded speech output (e.g., an encoded and/or compressed representation of the speech data, etc.). As another example, the machine-learned model(s) can process the speech data to generate an upscaled speech output (e.g., speech data that is higher quality than the input speech data, etc.). As another example, the machine-learned model(s) can process the speech data to generate a textual representation output (e.g., a textual representation of the input speech data, etc.). As another example, the machine-learned model(s) can process the speech data to generate a prediction output.

In some implementations, the training data used to train the machine-learned model(s) of the present disclosure can be partially labeled latent encoding data. The machine-learned model(s) can process the latent encoding data to generate an output. As an example, the machine-learned model(s) can process the latent encoding data to generate a recognition output. As another example, the machine-learned model(s) can process the latent encoding data to generate a reconstruction output. As another example, the machine-learned model(s) can process the latent encoding data to generate a search output. As another example, the machine-learned model(s) can process the latent encoding data to generate a reclustering output. As another example, the machine-learned model(s) can process the latent encoding data to generate a prediction output.

In some implementations, the input to the machine-learned model(s) of the present disclosure can be statistical data. The machine-learned model(s) can process the statistical data to generate an output. As an example, the machine-learned model(s) can process the statistical data to generate a recognition output. As another example, the machine-learned model(s) can process the statistical data to generate a prediction output. As another example, the machine-learned model(s) can process the statistical data to generate a classification output. As another example, the machine-learned model(s) can process the statistical data to generate a segmentation output. As another example, the machine-learned model(s) can process the statistical data to generate a segmentation output. As another example, the machine-learned model(s) can process the statistical data to generate a visualization output. As another example, the machine-learned model(s) can process the statistical data to generate a diagnostic output.

In some implementations, the input to the machine-learned model(s) of the present disclosure can be sensor data. The machine-learned model(s) can process the sensor data to generate an output. As an example, the machine-learned model(s) can process the sensor data to generate a recognition output. As another example, the machine-learned model(s) can process the sensor data to generate a prediction output. As another example, the machine-learned model(s) can process the sensor data to generate a classification output. As another example, the machine-learned model(s) can process the sensor data to generate a segmentation output. As another example, the machine-learned model(s) can process the sensor data to generate a segmentation output. As another example, the machine-learned model(s) can process the sensor data to generate a visualization output. As another example, the machine-learned model(s) can process the sensor data to generate a diagnostic output. As another example, the machine-learned model(s) can process the sensor data to generate a detection output.

In some cases, the machine-learned model(s) can be configured to perform a task that includes encoding input data for reliable and/or efficient transmission or storage (and/or corresponding decoding). For example, the task may be or otherwise include an audio compression task. The input may include audio data and the output may comprise compressed audio data. In another example, the input includes visual data (e.g. one or more images or videos), the output comprises compressed visual data, and the task is a visual data compression task. In another example, the task may comprise generating an embedding for input data (e.g. input audio or visual data).

In some cases, the training data can include partially labeled audio data representing a spoken utterance and the task is a speech recognition task. The output may comprise a text output which is mapped to the spoken utterance. In some cases, the task comprises encrypting or decrypting input data. In some cases, the task comprises a microprocessor performance task, such as branch prediction or memory address translation.

FIG.1Bdepicts a block diagram of an example computing device10that performs training of a machine-learned multi-class object classification model according to example embodiments of the present disclosure. The computing device10can be a user computing device or a server computing device.

The computing device10includes a number of applications (e.g., applications 1 through N). Each application contains its own machine learning library and machine-learned model(s). For example, each application can include a machine-learned model. Example applications include a text messaging application, an email application, a dictation application, a virtual keyboard application, a browser application, etc.

FIG.1Cdepicts a block diagram of an example computing device50that performs machine-learned multi-class object classification according to example embodiments of the present disclosure. The computing device50can be a user computing device or a server computing device.

The computing device50includes a number of applications (e.g., applications 1 through N). Each application is in communication with a central intelligence layer. Example applications include a text messaging application, an email application, a dictation application, a virtual keyboard application, a browser application, etc. In some implementations, each application can communicate with the central intelligence layer (and model(s) stored therein) using an API (e.g., a common API across all applications).

Example Model Arrangements

FIG.2depicts a block diagram of an example machine-learned multi-class object classification model200according to example embodiments of the present disclosure. In some implementations, the machine-learned multi-class object classification model200is trained to receive a set of input data204descriptive of an image and, as a result of receipt of the input data204, provide output data206that classifies one or more objects depicted in the image.

More particularly, the machine-learned multi-class object classification model200can be configured to classify a plurality of object classes. The machine-learned multi-class object classification model200can obtain input data204that includes image data depicting one or more objects. The machine-learned multi-class object classification model200can process the image data to obtain output data206. The output data206can include object classification data that classifies the one or more objects as belonging to one or more respective object classes. As an example, the output data204may include one or more object class annotations for the one or more objects depicted in the input data204. As another example, the output data204may include one or more image annotations that annotate the entire image depicted by the input data204as belonging to one or more classes. For example, the machine-learned multi-class object classification model200may label the one or more additional objects as being semantically related to a “nature” image (e.g., a rabbit object, a tree object, a boulder object, a grass object, etc.). Based on the object classification label(s), the image classification data may label the image depicted by the image data204as belonging to a “nature” image class.

FIG.3depicts a block diagram of an example machine-learned image analysis model300according to example embodiments of the present disclosure. The machine-learned image analysis model300is similar to machine-learned multi-class object classification model200ofFIG.2except that machine-learned image analysis model300further includes a machine-learned object recognition model302. The machine-learned object recognition model302can be operable to predict the presence of one or more objects in a portion of the input data204.

More particularly, the machine-learned multi-class object classification model304may be included as an object classification portion of the machine-learned image analysis model300(e.g., a conventional two-stage object detector model, etc.). Additionally, the machine-learned object recognition model302can be included as an object recognition portion of the machine-learned image analysis model300. For example, the machine-learned object recognition model302(e.g., a region proposal network (RPN), etc.) may obtain the input data204that includes image data and output a portion of the image data304(e.g., defined as a bounding box) predicted to contain an object (e.g., using an anchoring-based approach, etc.). The portion of the image data304can be processed with the machine-learned multi-class object classification model202to obtain the output data206that includes the object classification data.

FIG.4depicts a data flow diagram of a method400for training a machine-learned multi-class object classification model using partially labeled training data according to example embodiments of the present disclosure. Image data402(e.g., partially labeled training data, etc.) can be obtained that depicts three objects402A-402C that are each classified as belonging to a respective object class from a plurality of object classes. As depicted, the example objects402A-402C can respectively belong to a lion object class (e.g.,402A), a tiger object class (e.g.,402B), and a bear object class (e.g.,402C). Ground truth data404can be obtained alongside the image data402that includes a subset of object class annotations404A/404B that are respectively associated with a subset of object classes from a plurality of object classes. More particularly, the ground truth data404can include a ground truth lion class label404A (e.g., object class annotation) for the object402A and a ground truth tiger label404B (e.g., object class annotation) for the tiger object402B, but can lack a corresponding object class annotation for the bear object402C (e.g., a label for the region of the image that the bear is depicted in).

As depicted, the object class annotations404A-404B can be or otherwise include a bounding box that defines a region of the image data402and an associated class label (e.g.,404A/404B). Alternatively, or additionally, in some implementations, the ground truth data404can include object class annotation(s) that label the entire image depicted in the image data402as including or not including an object of a particular object class. As an example, a ground truth kangaroo label of the ground truth data404may indicate that the image data402does or does not depict a kangaroo.

In some implementations, the machine-learned multi-class object classification model406can process the image data402. Alternatively, in some implementations, the machine-learned multi-class object classification model406can process a portion of the image data402predicted to include an object. For example, the image data can be the portion of the image data402that includes the bear class object402C (e.g., the portion of the image data402that is not defined by a bounding box associated with labels404A-404B, etc.). The image data402can be processed to obtain object classification data408. The object classification data408output by the machine-learned multi-class object classification model406can include a multi-class classification output408A-408C for the object(s) (e.g.,402A-402C) that the machine-learned multi-class object classification model is configured to classify.

The object classification data408can include class output408A-408C for each of the classes that the machine-learned multi-class object classification model406is configured to classify (e.g., a lion class, a tiger class, a bear class, etc.). As an example, each of the class outputs408A-408C can include a prediction as to whether the object depicted in the image data402belongs to a respective class. For example, class 1 output408A may indicate that the object belongs to the lion class402A and class outputs408B/408C can indicate that the object does not belong to their respective classes402B/402C. As another example, each of the class outputs408A-408C can indicate probability that the object depicted in the image data (or the portion of the image data) belongs to each of the four classes. For example, class 1 output408A can indicate a 15% probability that the depicted object is a tiger, while class 2 output408B and class 3 output408C can indicate other probabilities that the object is a certain class.

The loss function410can be evaluated that evaluates a difference between the object classification data408and the subset of object class annotations of the ground truth data404(e.g., a difference between a predicted object class annotation408A-408C and a ground truth object class annotation408A-408B, etc.). More particularly, the loss function410can include a plurality of weighted loss signals410A-410C that are respectively associated with the plurality of object classes402A-402C (e.g., a weighted lion loss signal410A for a lion class402A, a weighted tiger loss signal410B for a tiger class402B, a weighted bear loss signal410C for a bear class402C, etc.). The weight of each of the weighted loss signals410A-410C can be based at least in part on the inclusion of the object class402A-402C associated with the respective loss signal410A-410C within the subset of object classes402A-402C.

The weighted lion loss signal410A and the weighted tiger loss signal410B are respectively associated with classes402A and402B that are included in the subset of classes402A/402B of the plurality of classes402A-402C (e.g., labeled in the ground truth data402with object class annotations404A/404B). The weighted bear loss signal410C is associated with a class402C that is not included in the subset of classes402A/402B of the plurality of classes402A-402C (e.g., not labeled in the ground truth data402with a corresponding object class annotation). As there is no object class annotation in the ground truth data404for the class402C, an accuracy (e.g., a loss) of class output408C for the bear class402C cannot necessarily be evaluated properly. As such, due to the unknown classification accuracy of output408C, the corresponding weight of the weighted bear loss signal410C can be lower relative to the weights of loss signals for which labels are provided (e.g.,410A and410B), therefore reducing the overall impact of the weighted bear loss signal410C on the final loss value412.

The final loss value412can be determined based at least in part on the weighted loss signals410A,410B, and410C. In some implementations, a weighted loss signal for a class that is not included in the subset of classes (e.g., a weighted loss signal for a class without a corresponding object class annotation) can be weighted such that the loss signal is excluded from determination of the final loss value412. As an example, the weight of the weighted loss signal410C can be a weight of zero, and the final loss value412can be based on each weighted loss signal with a weight above zero (e.g.,410A and410B). In some implementations, the weights of each of the weighted loss signals can be a normalized value. For example, the weight of the weighted bear loss signal410C can be a weight of 0.35 while the weight of weighted lion and tiger loss signals410A/410B can each be weighted 1 (e.g., to reduce the relevance of the weighted bear loss signal410C to the calculation of the final loss value, etc.). The final loss value412can be determined based on the respective weights of each of the loss signals410A-410C.

One or more parameter adjustments414can be generated based at least in part on the loss function410and/or the final loss value412, and can be applied to the machine-learned multi-class object classification model (e.g., using a gradient descent algorithm, etc.). In such fashion, a penalty to the machine-learned multi-class object classification model derived from loss value412can be reduced for a model output corresponding to a class without a corresponding class annotation, therefore facilitating the training of the machine-learned multi-class object classification model406with image data402that is only partially labeled (e.g., image data that includes objects without a corresponding label, etc.).

Example Methods

FIG.5depicts a flow chart diagram of an example method500to perform training of a machine-learned multi-class object classification model with partially labeled training data according to example embodiments of the present disclosure. AlthoughFIG.5depicts steps performed in a particular order for purposes of illustration and discussion, the methods of the present disclosure are not limited to the particularly illustrated order or arrangement. The various steps of the method500can be omitted, rearranged, combined, and/or adapted in various ways without deviating from the scope of the present disclosure.

At502, a computing system can obtain image data depicting objects and ground truth data including object class annotations. More particularly, image data can be obtained by the computing system that depicts one or more objects can be obtained alongside ground truth data. The ground truth data can include a subset of object class annotations (e.g., ground truth labels) that are respectively associated with a subset of object classes from a plurality of object classes. As an example, the image data can be or otherwise include one or more images (e.g., an image, a plurality of video frames, etc.) from a training dataset that is configured to be used to train a machine-learned classification model to recognize and classify a plurality of classes. The image from the training dataset can include four objects belonging to four separate classes. The subset of object class annotations can be or otherwise include annotations for the first two classes of the four separate classes. As another example, the ground truth data for a second image of the training dataset can include object class annotations for the remaining two classes of the four separate classes. In such fashion, the subset of class annotations included in the ground truth data can, in some implementations, include different classes from the plurality of classes for different images included in a training dataset.

In some implementations, the object class annotations can include a bounding box that defines a region of the image data and an associated class label. As an example, the image data may depict a lion object. The object class annotation can include a bounding box that defines the region of the image data that includes the lion and a corresponding “lion” label. Alternatively, or additionally, in some implementations, the object class annotation can label the entire image of the image data as including or not including a class. As an example, the image data may not depict a tiger. The object class annotation can indicate that a tiger is not depicted in the image data.

In some implementations, the image data can be a portion of image data from an image. As an example, the image data can be a portion of image data extracted from an image based on a prediction that the portion of image data depicts an object (e.g., extracted by a region proposal network (RPN), etc.). As another example, the image data can be a portion of image data that is not defined by a bounding box of an object class annotation. Alternatively, in some implementations, the image data can be or otherwise include the entirety of the depiction of the image data. As an example, the image data can be obtained and can subsequently be processed iteratively by the machine-learned multi-class object classification model to recognize regions that are predicted to include an object and subsequently or concurrently classify the object.

At504, the computing system can process the image data with a machine-learned multi-class object classification model. More particularly, the computing system can process the image data with the machine-learned multi-class object classification model to obtain object classification data. The machine-learned multi-class object classification model can be or otherwise include any sort of conventional machine-learned object classification model. As an example, the machine-learned model can be or can otherwise include one or more neural networks (e.g., deep neural networks, recurrent neural networks, graph neural networks, etc.) or the like. Neural networks (e.g., deep neural networks) can be feed-forward neural networks, convolutional neural networks, and/or various other types of neural networks.

The machine-learned multi-class object classification model can be configured to classify a plurality of object classes. More particularly, the machine-learned multi-class object classification model can be configured to process image data to classify an object depicted in the image data as belonging to a class from the plurality of classes. Additionally, or alternatively, in some implementations the machine-learned multi-class object classification model can be a portion (e.g., a component of a model, one or more layers of a model, etc.) of a machine-learned image analysis model that performs both object recognition and object classification. As an example, the machine-learned multi-class object classification model may be an object classification portion of a conventional two-stage object detector model (e.g., Faster-RCNN, etc.). For example, a first stage of the two-stage object detector model (e.g., a region proposal network (RPN), etc.) may obtain the image data and output a region of the image data (e.g., a region not defined by a bounding box, etc.) predicted to contain an object (e.g., using an anchoring-based approach, etc.). The region of the image data can be processed with the machine-learned multi-class object classification model to obtain the object classification data.

As another example, the machine-learned multi-class object classification model can be or otherwise be incorporated in a one-stage object detector model (e.g., a 1-stage single shot detector model, etc.) For example, the machine-learned multi-class object classification model can be or otherwise include a plurality of machine-learned layers (e.g., convolutional layer(s), activation layer(s), etc.) that predict region(s) that may include a predicted object that can subsequently be processed with additional classification layers of the multi-class object classification model to generate the object classification data. For another example, the machine-learned multi-class object classification model can include a plurality of layers that predict a region of the image data to include a predicted object and simultaneously classify the predicted object. In such fashion, the method of training the machine-learned multi-class object classification model can be applied to any conventional machine-learned image analysis model (e.g., a single shot detector (SSD) model, a you-only-look-once (YOLO) model, a Faster-RCNN model, etc.).

The object classification data output by the machine-learned multi-class object classification model can provide a multi-class classification output for an object depicted in the image data. As an example, a portion of the image may depict a bear object. The machine-learned multi-class object classification model can be configured to classify an object as belonging to one of four classes (e.g., a bear, a tiger, a kangaroo, a lion, etc.). The object classification data may include a plurality of predicted object class annotations indicative of whether an object belongs to each of the classes (e.g., bear 1, tiger 0, kangaroo 0, lion 0, etc.). Alternatively, in some implementations, the object classification data may include a plurality of object class probability predictions that the object depicted in the image data belongs to each of the four classes (e.g., 80% bear, 15% tiger, 15% kangaroo, 10% lion, etc.). In some implementations, the object classification data may include a single indication that the object depicted in the image data belongs to a class. As an example, the object classification data may be or otherwise include a predicted class annotation (e.g., a “bear” class annotation, etc.). As such, the object classification data can include a plurality of predictive probability outputs for a respective plurality of classes and/or a predicted class annotation for an object depicted in the image data or a portion of the image data.

At506, the computing system can evaluate a loss function. More particularly, the computing system can evaluate a loss function that evaluates a multi-class classification loss. The multi-class classification loss can include a difference between the object classification data and the subset of object class annotations (e.g., a difference between a predicted object class annotation and a ground truth object class annotation, etc.). More particularly, the loss function can include a plurality of weighted loss signals that are respectively associated with the plurality of object classes (e.g., a bear weighted loss signal for a bear class, a lion weighted loss signal for a lion, etc.). The weight of each of the weighted loss signals can be based at least in part on the inclusion of the object class associated with the respective loss signal within the subset of object classes.

As an example, a first weighted loss signal can be associated with a first class from the plurality of object classes that is not included in the subset of object classes. For example, the first class can be a kangaroo class and the subset of object classes can include a bear class, a lion class, and a tiger class. Since the first class is not included in the subset of classes, the weight of the first loss signal can be weighted as to reduce the impact of the first loss signal relative to the loss function. In some implementations, the weights of each of the weighted loss signals can be a normalized value. For example, the weight of the first loss signal can be a weight of 0 while the weight of a second loss signal associated with a class included in the subset of classes can be weighted 1 (e.g., to eliminate the loss signal from the loss function, etc.). For another example, the weight of the first loss signal can be a weight of 0.35 while the weight of a second loss signal associated with a class included in the subset of classes can be weighted 1 (e.g., to reduce the relevance of the first loss signal with respect to the second loss signal, etc.). In such fashion, the weights of the weighted loss signals can be configured to reduce or eliminate the impact of the respective loss signals that are not associated with classes included in the subset of classes (e.g., classes with associated object class annotations (labels), etc.).

At508, the computing system can adjust parameters of the machine-learned multi-class object classification model. More particularly, the computing system can adjust one or more parameters of the machine-learned multi-class object classification model based at least in part on the loss function. More particularly, the parameter(s) of the machine-learned multi-class object classification model can be adjusted based on each of the weighted loss signals and their respective weights. In some implementations, the parameter(s) of the machine-learned multi-class object classification model can be adjusted proportionally to the weights of each of the weighted loss signals of the loss function. As an example, the loss function can be utilized to calculate a final loss value based on an evaluation of the difference between the object classification data and the ground truth data. The final loss value can be based proportionally on the weights of the respective weighted loss signals. For example, a weighted loss signal with a weight of zero may contribute nothing to calculation of the final loss value. The loss value can be back propagated through the machine-learned multi-class classification model, and one or more parameters of the model can be adjusted based on the final loss value (e.g., using a gradient descent algorithm, etc.). In such fashion, the adjustments to parameter(s) of the model can be proportional to the weights of the weighted loss signals, therefore reducing the impact of loss signals associated with unlabeled classes during training of the model.

In some implementations, evaluating the loss function can include evaluating a subset of weighted loss signals of the loss function that are respectively associated with the subset of object classes. More particularly, the loss function can include only the weighted loss signals that are associated with the object classes that are labeled (e.g., the subset of object class annotations of the ground truth data, etc.). In such fashion, the loss function can exclude any loss signal associated with an object class that is not annotated by the subset of object class annotations (e.g., is not labeled in the training data, etc.).

In some implementations, the machine-learned multi-class object classification model can be utilized after training. More particularly, the computing system can obtain additional image data depicting one or more additional objects. The image data can be processed using the machine-learned multi-class object classification model to obtain an image classification output. In some implementations, the image classification output can include one or more labels descriptive of the additional image data. As an example, the image classification output may include one or more object class annotations for the one or more additional objects. As another example, the image classification output may include one or more image annotations that annotate the entire image as belonging to one or more classes. For example, the machine-learned multi-class object classification model may label the one or more additional objects as being semantically related to a “nature” image (e.g., a rabbit object, a tree object, a boulder object, a grass object, etc.). Based on the object classification label(s), the image classification data may label the image as belonging to a “nature” image

Additional Disclosure