Editable Image Generation with Consistent Unsupervised Disentanglement Based on GAN

Abstract

Generative adversarial networks (GANs) are often used to generate realistic images, and GANs are effective in fitting high-dimensional probability distributions. However, during training, they often produce model collapse, which is the inability of the generative model to map the input noise to the real data distribution. In this work, we propose a model for disentanglement and mitigating model collapse inspired by the relationship between Hessian and Jacobian matrices. This is a concise framework for producing few modifications to the original model while facilitating the disentanglement. Compared to the pre-improvement generative models, our approach modifies the original model architecture only marginally and does not change the training method. Our method shows consistent resistance to model collapse on some image datasets, while outperforming the pre-improvement method in terms of disentanglement.