Abstract: Apparatuses, systems, and techniques to train a generative model based at least in part on a private dataset. In at least one embodiment, the generative model is trained based at least in part on a differentially private Sinkhorn algorithm, for example, using backpropagation with gradient descent to determine a gradient of a set of parameters of the generative models and modifying the set of parameters based at least in part on the gradient.

Abstract: Apparatuses, systems, and techniques to generate labels for images using generative adversarial networks. In at least one embodiment, one or more objects in an input image are identified using one or more generative adversarial networks (GANs) and a synthetic version of the input image and one or more labels corresponding to the one or more objects within the synthetic version of the input image are generated using the GANs.

Abstract: Apparatuses, systems, and techniques to animate objects in computer-generated graphics. In at least one embodiment, one or more neural networks are trained to identify one or more forces to be applied to one or more objects based, at least in part, on training data corresponding to two or more aspects of motion of the one or more objects.

Abstract: Apparatuses, systems, and techniques to determine pixel-level labels of a synthetic image. In at least one embodiment, the synthetic image is generated by one or more generative networks and the pixel-level labels are generated using a combination of data output by a plurality of layers of the generative networks.

Abstract: Apparatuses, systems, and techniques to generate complete depictions of objects based on a partial depiction of the object. In at least one embodiment, an image of a complete object is generated by one or more neural networks, based on an image of a portion of the object, using an encoder of the one or more neural networks trained using training data generated from output of a decoder of the one or more neural networks.

Abstract: The present invention relates to a computer-implemented system and method for generation of an interactive color workspace, the method including: generating a workspace; receiving successive inputs from a user to create two or more controllable color swatches, the controllable color swatches including an associated color; generating the controllable color swatches on the workspace; and upon receiving a command from a user to select and associate at least two of the controllable color swatches, generating a gradient of colors with the colors of the selected controllable color swatches as endpoints to the gradient.

Abstract: Apparatuses, systems, and techniques are presented to generate view-specific representations of an object or environment. In at least one embodiment, one or more neural networks are used to generate one or more images based, at least in part, on two or more two-dimensional (2D) images having different frames of reference.

Abstract: Apparatuses, systems, and techniques are presented to generate a simulated environment. In at least one embodiment, one or more neural networks are used to generate a simulated environment based, at least in part, on stored information associated with objects within the simulated environment.

Abstract: There is provided a system and method for color representation generation. In an aspect, the method includes: receiving three base colors; receiving a patchwork parameter; and generating a color representation having each of the three base colors at a vertex of a triangular face, the triangular face having a color distribution therein, the color distribution discretized into discrete portions, the amount of discretization based on the patchwork parameter, each discrete portion having an interpolated color determined to be a combination of the base colors at respective coordinates of such discrete portion. In further aspects, one or more color representations are generated based on an input image and can be used to modify colors of a reconstructed image.

Abstract: A rule set or scene grammar can be used to generate a scene graph that represents the structure and visual parameters of objects in a scene. A renderer can take this scene graph as input and, with a library of content for assets identified in the scene graph, can generate a synthetic image of a scene that has the desired scene structure without the need for manual placement of any of the objects in the scene. Images or environments synthesized in this way can be used to, for example, generate training data for real world navigational applications, as well as to generate virtual worlds for games or virtual reality experiences.

Abstract: Approaches are presented for training an inverse graphics network. An image synthesis network can generate training data for an inverse graphics network. In turn, the inverse graphics network can teach the synthesis network about the physical three-dimensional (3D) controls. Such an approach can provide for accurate 3D reconstruction of objects from 2D images using the trained inverse graphics network, while requiring little annotation of the provided training data. Such an approach can extract and disentangle 3D knowledge learned by generative models by utilizing differentiable renderers, enabling a disentangled generative model to function as a controllable 3D “neural renderer,” complementing traditional graphics renderers.

Abstract: A computer-implemented method for selecting training data for a neural network, which includes representing a dataset with a mixture of experts model, the mixture of experts model comprising one or more trained neural networks; and generating an application dataset based on one or more performance indicators of one or more of the trained neural networks. Representing the dataset with the mixture of experts model can include partitioning the dataset into one or more data subsets and training one or more neural networks each on one of the data subsets to generate the one or more trained neural networks. A platform for training a neural network and a computer product for carrying out the steps of the method are also described.

Abstract: There is provided a system and method for color representation generation. In an aspect, the method includes: receiving three base colors; receiving a patchwork parameter; and generating a color representation having each of the three base colors at a vertex of a triangular face, the triangular face having a color distribution therein, the color distribution discretized into discrete portions, the amount of discretization based on the patchwork parameter, each discrete portion having an interpolated color determined to be a combination of the base colors at respective coordinates of such discrete portion. In further aspects, one or more color representations are generated based on an input image and can be used to modify colors of a reconstructed image.

Abstract: There is provided a system and method for color representation generation. In an aspect, the method includes: receiving three base colors; receiving a patchwork parameter; and generating a color representation having each of the three base colors at a vertex of a triangular face, the triangular face having a color distribution therein, the color distribution discretized into discrete portions, the amount of discretization based on the patchwork parameter, each discrete portion having an interpolated color determined to be a combination of the base colors at respective coordinates of such discrete portion. In further aspects, one or more color representations are generated based on an input image and can be used to modify colors of a reconstructed image.

Abstract: The present invention relates to a computer-implemented system and method for generation of an interactive color workspace, the method including: generating a workspace; receiving successive inputs from a user to create two or more controllable color swatches, the controllable color swatches including an associated color; generating the controllable color swatches on the workspace; and upon receiving a command from a user to select and associate at least two of the controllable color swatches, generating a gradient of colors with the colors of the selected controllable color swatches as endpoints to the gradient.

Abstract: A generative model can be used for generation of spatial layouts and graphs. Such a model can progressively grow these layouts and graphs based on local statistics, where nodes can represent spatial control points of the layout, and edges can represent segments or paths between nodes, such as may correspond to road segments. A generative model can utilize an encoder-decoder architecture where the encoder is a recurrent neural network (RNN) that encodes local incoming paths into a node and the decoder is another RNN that generates outgoing nodes and edges connecting an existing node to the newly generated nodes. Generation is done iteratively, and can finish once all nodes are visited or another end condition is satisfied. Such a model can generate layouts by additionally conditioning on a set of attributes, giving control to a user in generating the layout.

Abstract: Various types of image analysis benefit from a multi-stream architecture that allows the analysis to consider shape data. A shape stream can process image data in parallel with a primary stream, where data from layers of a network in the primary stream is provided as input to a network of the shape stream. The shape data can be fused with the primary analysis data to produce more accurate output, such as to produce accurate boundary information when the shape data is used with semantic segmentation data produced by the primary stream. A gate structure can be used to connect the intermediate layers of the primary and shape streams, using higher level activations to gate lower level activations in the shape stream. Such a gate structure can help focus the shape stream on the relevant information and reduces any additional weight of the shape stream.

Abstract: The present invention relates generally to object annotation, specifically to polygonal annotations of objects. Described are methods of annotating an object including steps of receiving an image depicting an object, generating a set of image features using a CNN encoder implemented on one or more computers, and producing a polygon object annotation via a recurrent decoder or a Graph Neural Network. The recurrent decoder may include a recurrent neural network, a graph neural network or a gated graph neural network. A system for annotating an object and a method of training an object annotation system are also described.

Abstract: In various examples, a generative model is used to synthesize datasets for use in training a downstream machine learning model to perform an associated task. The synthesized datasets may be generated by sampling a scene graph from a scene grammar—such as a probabilistic grammar—and applying the scene graph to the generative model to compute updated scene graphs more representative of object attribute distributions of real-world datasets. The downstream machine learning model may be validated against a real-world validation dataset, and the performance of the model on the real-world validation dataset may be used as an additional factor in further training or fine-tuning the generative model for generating the synthesized datasets specific to the task of the downstream machine learning model.

Abstract: The present invention relates generally to object annotation, specifically to polygonal annotations of objects. Described are methods of annotating an object including steps of receiving an image depicting an object, generating a set of image features using a CNN encoder implemented on one or more computers, and producing a polygon object annotation via a recurrent decoder or a Graph Neural Network. The recurrent decoder may include a recurrent neural network, a graph neural network or a gated graph neural network. A system for annotating an object and a method of training an object annotation system are also described.