NVIDIA Corporation
TRAINING A LATENT-VARIABLE GENERATIVE MODEL WITH A NOISE CONTRASTIVE PRIOR

Abstract:

One embodiment of the present invention sets forth a technique for creating a generative model. The technique includes performing one or more operations based on a plurality of training images to generate an encoder network and a prior network, wherein the encoder network converts each image in the training images into a set of visual attributes, and the prior network learns a distribution of the visual attributes across the training images. The technique also includes training one or more classifiers to distinguish between values for the visual attributes generated by the encoder network and values for the visual attributes selected from the distribution learned by the prior network. The technique further includes combining the prior network and the classifier(s) to produce a trained prior component that, in operation, produces one or more values for the visual attributes to generate a new image that is not in the training images.