Generating data based on pre-trained models using generative adversarial models

Abstract

Techniques for generator model training are provided. A classifier model that was trained using one or more data samples in a target class is received, and a generative adversarial network (GAN) is trained to generate simulated data samples for the target class, comprising: generating a first simulated data sample using a generator model, computing a first discriminator loss by processing the first simulated data sample using a discriminator model, computing a classifier loss by processing the first simulated data sample using the classifier model, and refining the generator model based on the first discriminator loss and the classifier loss.