Latent-variable generative model with a noise contrastive prior

A computer-implemented method for generating data using a generative model, the method comprising:

• sampling one or more first values from a distribution of latent variables learned by a prior network included in the generative model;
• applying a reweighting factor to the one or more first values in order to generate one or more second values for the latent variables, wherein the reweighting factor is generated based on one or more classifiers that operate to distinguish between values sampled from the distribution and values for the latent variables generated via an encoder network included in the generative model;
• and
• performing one or more decoding operations on the one or more second values via a decoder network included in the generative model to produce generative output.

A non-transitory computer readable medium storing instructions that, when executed by a processor, cause the processor to perform the steps of:

• sampling one or more first values from a distribution of latent variables learned by a prior component included in a generative model;
• applying a reweighting factor to the one or more first values in order to generate one or more second values for the latent variables, wherein the reweighting factor is generated based on one or more classifiers that operate to distinguish between values sampled from the distribution and values for the latent variables generated via an encoder network included in the generative model;
• and
• performing one or more decoding operations on the one or more second values via a decoder network included in the generative model to produce the generative output.