MCGAN: Enhancing GAN Training with Regression-Based Generator Loss

TL;DR

MCGAN introduces a regression-based loss function to stabilize and improve GAN training, demonstrating enhanced performance and versatility across multiple data types and datasets.

Contribution

This paper proposes MCGAN, a novel GAN training method using a regression loss to improve stability and performance, requiring weaker discriminator conditions.

Findings

Improved training stability across diverse datasets.

Enhanced image and data quality with MCGAN.

Versatile applicability to various GAN architectures.

Abstract

Generative adversarial networks (GANs) have emerged as a powerful tool for generating high-fidelity data. However, the main bottleneck of existing approaches is the lack of supervision on the generator training, which often results in undamped oscillation and unsatisfactory performance. To address this issue, we propose an algorithm called Monte Carlo GAN (MCGAN). This approach, utilizing an innovative generative loss function, termly the regression loss, reformulates the generator training as a regression task and enables the generator training by minimizing the mean squared error between the discriminator's output of real data and the expected discriminator of fake data. We demonstrate the desirable analytic properties of the regression loss, including discriminability and optimality, and show that our method requires a weaker condition on the discriminator for effective generator training. These properties justify the strength of this approach to improve the training stability while retaining the optimality of GAN by leveraging strong supervision of the regression loss. Extensive experiments on diverse datasets, including image data (CIFAR-10/100, FFHQ256, ImageNet, and LSUN Bedroom), time series data (VAR and stock data) and video data, are conducted to demonstrate the flexibility and effectiveness of our proposed MCGAN. Numerical results show that the proposed MCGAN is versatile in enhancing a variety of backbone GAN models and achieves consistent and significant improvement in terms of quality, accuracy, training stability, and learned latent space.