Abstract: Apparatuses, systems, and techniques to enhance video are disclosed. In at least one embodiment, one or more neural networks are used to create, from a first video, a second video having one or more additional video frames.

Abstract: Apparatuses, systems, and techniques to perform unsupervised keypoint or landmark learning using one or more neural networks. In at least one embodiment, one or more neural networks use pose and appearance information to construct a foreground and a background, which are then used to reconstruct an input image and determine loss values to train the one or more neural networks.

Abstract: Apparatuses, systems, and techniques to scale textured images using a Fourier transform in conjunction with one or more neural networks. In at least one embodiment, a neural network generates an expanded image from an input image by applying a Fourier transform to one or more feature maps generated by said neural network and up-scaling one or more resulting frequency domain feature maps before generating an expanded output image based on up-scaled feature maps.

Abstract: A style transfer neural network may be used to generate stylized synthetic images, where real images provide the style (e.g., seasons, weather, lighting) for transfer to synthetic images. The stylized synthetic images may then be used to train a recognition neural network. In turn, the trained neural network may be used to predict semantic labels for the real images, providing recognition data for the real images. Finally, the real training dataset (real images and predicted recognition data) and the synthetic training dataset are used by the style transfer neural network to generate stylized synthetic images. The training of the neural network, prediction of recognition data for the real images, and stylizing of the synthetic images may be repeated for a number of iterations. The stylization operation more closely aligns a covariate of the synthetic images to the covariate of the real images, improving accuracy of the recognition neural network.

Abstract: Apparatuses, systems, and techniques to enhance video are disclosed. In at least one embodiment, one or more neural networks are used to create, from a first video, a second video having one or more additional video frames.

Abstract: Apparatuses, systems, and techniques to enhance video. In at least one embodiment, one or more neural networks are used to create, from a first video, a second video having a higher frame rate, higher resolution, or reduced number of missing or corrupt video frames.

Abstract: A style transfer neural network may be used to generate stylized synthetic images, where real images provide the style (e.g., seasons, weather, lighting) for transfer to synthetic images. The stylized synthetic images may then be used to train a recognition neural network. In turn, the trained neural network may be used to predict semantic labels for the real images, providing recognition data for the real images. Finally, the real training dataset (real images and predicted recognition data) and the synthetic training dataset are used by the style transfer neural network to generate stylized synthetic images. The training of the neural network, prediction of recognition data for the real images, and stylizing of the synthetic images may be repeated for a number of iterations. The stylization operation more closely aligns a covariate of the synthetic images to the covariate of the real images, improving accuracy of the recognition neural network.

Abstract: A coprocessor (PL) is disclosed. The PL includes a memory router, at least one collection block that is configured to transfer data to/from the memory router, each collection block includes a collection router that is configured to i) transfer data to/from the memory router, ii) transfer data to/from at least one collection router of a neighboring collection block, and iii) transfer data to/from blocks within the collection block, and at least one programmable operator that is configured to i) transfer data to/from the collection router, and ii) perform a programmable operation on data received from the collection router.

Abstract: A coprocessor (PL) is disclosed. The PL includes a memory router, at least one collection block that is configured to transfer data to/from the memory router, each collection block includes a collection router that is configured to i) transfer data to/from the memory router, ii) transfer data to/from at least one collection router of a neighboring collection block, and iii) transfer data to/from blocks within the collection block, and at least one programmable operator that is configured to i) transfer data to/from the collection router, and ii) perform a programmable operation on data received from the collection router.

Abstract: A coprocessor (PL) is disclosed. The PL includes a memory router, at least one collection block that is configured to transfer data to/from the memory router, each collection block includes a collection router that is configured to i) transfer data to/from the memory router, ii) transfer data to/from at least one collection router of a neighboring collection block, and iii) transfer data to/from blocks within the collection block, and at least one programmable operator that is configured to i) transfer data to/from the collection router, and ii) perform a programmable operation on data received from the collection router.

Abstract: A coprocessor (PL) is disclosed. The PL includes a memory router, at least one collection block that is configured to transfer data to/from the memory router, each collection block includes a collection router that is configured to i) transfer data to/from the memory router, ii) transfer data to/from at least one collection router of a neighboring collection block, and iii) transfer data to/from blocks within the collection block, and at least one programmable operator that is configured to i) transfer data to/from the collection router, and ii) perform a programmable operation on data received from the collection router.