AUTOENCODING GENERATIVE ADVERSARIAL NETWORK FOR AUGMENTING TRAINING DATA USABLE TO TRAIN PREDICTIVE MODELS

摘要

Techniques for using a deep generative model to generate synthetic data sets that can be used to boost the performance of a discriminative model are described. In an example, an autoencoding generative adversarial network (AEGAN) is trained to generate the synthetic data sets. The AEGAN includes an autoencoding network and a generative adversarial network (GAN) that share a generator. The generator learns how to the generate synthetic data sets based on a data distribution from a latent space. Upon training the AEGAN, the generator generates the synthetic data sets. In turn, the synthetic data sets are used to train a predictive model, such as a convolutional neural network for gaze prediction.