Abstract: One embodiment of the present invention sets forth a technique for generating images (or other generative output). The technique includes determining one or more first values for a set of visual attributes included in a plurality of training images, wherein the set of visual attributes is encoded via a prior network. The technique also includes applying a reweighting factor to the first value(s) to generate one or more second values for the set of visual attributes, wherein the second value(s) represent the first value(s) shifted towards one or more third values for the set of visual attributes, wherein the one or more third values have been generated via an encoder network. The technique further includes performing one or more decoding operations on the second value(s) via a decoder network to generate a new image that is not included in the plurality of training images.

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.