Abstract: Apparatuses, systems, and techniques to enhance video are disclosed. In at least one embodiment, one or more neural networks are used to create a higher resolution video using upsampled frames from a lower resolution video.

Abstract: Apparatuses, systems, and techniques are presented to generate images with one or more visual effects applied. In at least one embodiment, one or more visual effects are applied to one or more images having a resolution that is less than a first resolution and those visual effects approximated for one or more images having a resolution that is greater than or equal to the first resolution.

Abstract: Apparatuses, systems, and techniques to generate interpolated video frames. In at least one embodiment, an interpolated video frame is generated based, at least in part, on a first set of pixel data sampled from a first video frame, and a second set of pixel data sampled from a second video frame based, at least in part, on a set of forward pointing motion vectors from the first video frame to the second video frame.

Abstract: Apparatuses, systems, and techniques to generate interpolated video frames. In at least one embodiment, an interpolated video frame is generated based, at least in part, on one of a plurality of possible motions of one or more objects from a first video frame to a second video frame.

Abstract: Apparatuses, systems, and techniques are presented to generate images with one or more visual effects applied. In at least one embodiment, one or more visual effects are applied to one or more images having a resolution that is less than a first resolution and those visual effects approximated for one or more images having a resolution that is greater than or equal to the first resolution.

Abstract: Apparatuses, systems, and techniques for texture synthesis from small input textures in images using convolutional neural networks. In at least one embodiment, one or more convolutional layers are used in conjunction with one or more transposed convolution operations to generate a large textured output image from a small input textured image while preserving global features and texture, according to various novel techniques described herein.

Abstract: Apparatuses, systems, and techniques to enhance video are disclosed. In at least one embodiment, one or more neural networks are used to create a higher resolution video using upsampled frames from a lower resolution video.

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: Provided is a method for preparing rebaudioside M by using an enzyme method. In the method, rebaudioside A or rebaudioside D is used as a substrate; and in the existence of a glucosyl donor, rebaudioside M is generated by means of reaction of the substrate under the catalysis of UDP-glucosyl transferase and/or recombinant cells containing the UDP-glucosyl transferase.

Abstract: A neural network architecture is disclosed for performing video frame prediction using a sequence of video frames and corresponding pairwise optical flows. The neural network processes the sequence of video frames and optical flows utilizing three-dimensional convolution operations, where time (or multiple video frames in the sequence of video frames) provides the third dimension in addition to the two-dimensional pixel space of the video frames. The neural network generates a set of parameters used to predict a next video frame in the sequence of video frames by sampling a previous video frame utilizing spatially-displaced convolution operations. In one embodiment, the set of parameters includes a displacement vector and at least one convolution kernel per pixel. Generating a pixel value in the next video frame includes applying the convolution kernel to a corresponding patch of pixels in the previous video frame based on the displacement vector.

Abstract: A neural network architecture is disclosed for performing image in-painting using partial convolution operations. The neural network processes an image and a corresponding mask that identifies holes in the image utilizing partial convolution operations, where the mask is used by the partial convolution operation to zero out coefficients of the convolution kernel corresponding to invalid pixel data for the holes. The mask is updated after each partial convolution operation is performed in an encoder section of the neural network. In one embodiment, the neural network is implemented using an encoder-decoder framework with skip links to forward representations of the features at different sections of the encoder to corresponding sections of the decoder.

Abstract: Provided is a method for preparing rebaudioside M by using an enzyme method. In the method, rebaudioside A or rebaudioside D is used as a substrate; and in the existence of a glucosyl donor, rebaudioside M is generated by means of reaction of the substrate under the catalysis of UDP-glucosyl transferase and/or recombinant cells containing the UDP-glucosyl transferase.

Abstract: Provided is a method for preparing rebaudioside M by using an enzyme method. In the method, rebaudioside A or rebaudioside D is used as a substrate; and in the existence of a glucosyl donor, rebaudioside M is generated by means of reaction of the substrate under the catalysis of UDP-glucosyl transferase and/or recombinant cells containing the UDP-glucosyl transferase.