Abstract: A fractional training process can be performed training images to an instance of a machine-learned generative image model to obtain a partially trained instance of the model. A fractional optimization process can be performed with the partially trained instance to an instance of a machine-learned three-dimensional (3D) implicit representation model obtain a partially optimized instance of the model. Based on the plurality of training images, pseudo multi-view subject images can be generated with the partially optimized instance of the 3D implicit representation model and a fully trained instance of the generative image model; The partially trained instance of the model can be trained with a set of training data. The partially optimized instance of the machine-learned 3D implicit representation model can be trained with the machine-learned multi-view image model.

Abstract: The present disclosure provides a computer-implemented method for determining an optimized list of sets of hyperparameter values for application to an additional machine learning task. The method includes obtaining data describing a plurality of different machine learning tasks. The method includes obtaining a plurality of candidate sets of hyperparameter values. The method includes determining an ordered list of sets of hyperparameters selected from the plurality of candidate sets of hyperparameter values, wherein the ordered list of sets of hyperparameters minimizes an aggregate loss over the plurality of different machine learning tasks. The method includes storing the ordered list of sets of hyperparameters for use in training an additional machine learning model to perform an additional machine learning task.

Abstract: Example embodiments may allow for the efficient, edge-preserving filtering, upsampling, or other processing of image data with respect to a reference image. A cost-minimization problem to generate an output image from the input array is mapped onto regularly-spaced vertices in a multidimensional vertex space. This mapping is based on an association between pixels of the reference image and the vertices, and between elements of the input array and the pixels of the reference image. The problem is them solved to determine vertex disparity values for each of the vertices. Pixels of the output image can be determined based on determined vertex disparity values for respective one or more vertices associated with each of the pixels. This fast, efficient image processing method can be used to enable edge-preserving image upsampling, image colorization, semantic segmentation of image contents, image filtering or de-noising, or other applications.