A study of traits that affect learnability in GANs
This research addresses the challenging problem of training GANs by identifying key traits affecting learnability, which is significant for researchers and practitioners working with generative models.
This paper empirically investigates the traits that influence learnability in Generative Adversarial Networks (GANs) using parameterized synthetic datasets. The study aims to deduce a simple theory from experimental observations regarding GAN training challenges.
Generative Adversarial Networks GANs are algorithmic architectures that use two neural networks, pitting one against the opposite so as to come up with new, synthetic instances of data that can pass for real data. Training a GAN is a challenging problem which requires us to apply advanced techniques like hyperparameter tuning, architecture engineering etc. Many different losses, regularization and normalization schemes, network architectures have been proposed to solve this challenging problem for different types of datasets. It becomes necessary to understand the experimental observations and deduce a simple theory for it. In this paper, we perform empirical experiments using parameterized synthetic datasets to probe what traits affect learnability.