Abstract: Described is a system performing operations comprising deriving, based on an evaluation of variations of a first machine learning model, a second machine learning model, the second machine learning model having layers with precisions assigned based on the evaluation, training the second machine learning model to reduce error between a first test output generated by the first machine learning model based on a first test input and a second test output generated by the second machine learning model based on the first test input, training the second machine learning model to reduce error between a third test output generated by the second machine learning model based on a second test input and a ground truth output associated with the second test input, providing an input for the second machine learning model, and generating an output using the second machine learning model based on the input.

Abstract: The present disclosure addresses technological challenges arising in the field of artificial intelligence (AI) with respect to inefficient use of computing resources and runtime delay. In particular, the present disclosure provides for development of a machine learning model that generates an image sample for a video in a single forward pass. The development of this machine learning model uses an adversarial training approach involving training two machine learning models, a generator model and a discriminator model. With the generator model trained in this way, the generator model can be used to generate image samples for a video in a single forward pass.

Abstract: A neural light field (NeLF) that runs real-time on mobile devices for neural rendering of three dimensional (3D) scenes, referred to as MobileR2L. The MobileR2L architecture runs efficiently on mobile devices with low latency and small size, and it achieves high-resolution generation while maintaining real-time inference for both synthetic and real-world 3D scenes on mobile devices. The MobileR2L has a network backbone including a convolutional layer embedding an input image at a resolution, residual blocks uploading the embedded image, and super-resolution modules receiving the uploaded embedded image and rendering an output image having a higher resolution than the embedded image. The convolution layer generates a number of rays equal to a number of pixels in the input image, where a partial number of the rays is uploaded to the super-resolution modules.

Abstract: A neural light field (NeLF) that runs real-time on mobile devices for neural rendering of three dimensional (3D) scenes, referred to as MobileR2L. The MobileR2L architecture runs efficiently on mobile devices with low latency and small size, and it achieves high-resolution generation while maintaining real-time inference for both synthetic and real-world 3D scenes on mobile devices. The MobileR2L has a network backbone including a convolutional layer embedding an input image at a resolution, residual blocks uploading the embedded image, and super-resolution modules receiving the uploaded embedded image and rendering an output image having a higher resolution than the embedded image. The convolution layer generates a number of rays equal to a number of pixels in the input image, where a partial number of the rays is uploaded to the super-resolution modules.

Abstract: An asymmetrically distributed convolution-attention neural network (AsCAN) includes a simple hybrid architecture in which the number of convolutional and transformer blocks is asymmetrically distributed in different processing stages. AsCAN adopts more convolutional blocks in the early processing stages, where the feature maps have relatively large spatial sizes, and more transformer blocks at the later processing stages. Transformer layers are incorporated in the early processing stages as well, except that fewer transformer blocks are used compared to convolutions in the early part. This trend is reversed at the lower resolution in the later processing stages. This uneven distribution of the convolutional and transformer blocks yields better throughput due to improved accelerator utilization at various batch sizes during the inference stage.

Abstract: A neural light field (NeLF) that runs real-time on mobile devices for neural rendering of three dimensional (3D) scenes, referred to as MobileR2L. The MobileR2L architecture runs efficiently on mobile devices with low latency and small size, and it achieves high-resolution generation while maintaining real-time inference for both synthetic and real-world 3D scenes on mobile devices. The MobileR2L has a network backbone including a convolutional layer embedding an input image at a resolution, residual blocks uploading the embedded image, and super-resolution modules receiving the uploaded embedded image and rendering an output image having a higher resolution than the embedded image. The convolution layer generates a number of rays equal to a number of pixels in the input image, where a partial number of the rays is uploaded to the super-resolution modules.