Training a latent-variable generative model with a noise contrastive prior

A computer-implemented method for creating a generative model, the method comprising:

• performing one or more operations based on a training dataset to generate a trained encoder network and a trained prior network, wherein the trained encoder network converts a plurality of data points included in the training dataset into a set of latent variables, and the trained prior network learns a distribution of the set of latent variables across the training dataset;
• performing one or more operations to train one or more classifiers to distinguish between values for the set of latent variables generated via the trained encoder network and values sampled from the distribution learned by the trained prior network;
• and
• creating a trained prior component based on the trained prior network and one or more classifiers, wherein the trained prior component applies a reweighting factor to one or more first values sampled from the distribution learned by the trained prior network to generate one or more second values for the set of latent variables,
• wherein, in operation, the trained prior component produces the one or more second values in order to generate a new data point that is not included in the training dataset.

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

• performing one or more operations based on a training dataset to train a generative model, wherein the generative model includes a first component that converts a plurality of data points included in the training dataset into a set of latent variables and a second component that generates a prior distribution of the set of latent variables across the training dataset;
• performing one or more operations to train one or more classifiers to distinguish between values for the set of latent variables generated via the first component and values sampled from the prior distribution;
• and
• creating a trained prior component based on the second component and one or more classifiers, wherein the trained prior component applies a reweighting factor to one or more first values sampled from the prior distribution to generate one or more second values for the set of latent variables, wherein the reweighting factor is determined based on output generated by the one or more classifiers from the one or more first values,
• wherein, in operation, the trained prior component produces the one or more second values in order to generate a new data point that is not included in the training dataset.