Evolution and effectiveness of loss functions in generative adversarial networks

Abstract

Recently, the evolution of Generative Adversarial Networks (GANs) has embarked on a journey of revolutionizing the field of artificial and computational intelligence. To improve the generating ability of GANs, various loss functions are introduced to measure the degree of similarity between the samples generated by the generator and the real data samples, and the effectiveness of the loss functions in improving the generating ability of GANs. In this paper, we present a detailed survey for the loss functions used in GANs, and provide a critical analysis on the pros and cons of these loss functions. First, the basic theory of GANs along with the training mechanism are introduced. Then, the most commonly used loss functions in GANs are introduced and analyzed. Third, the experimental analyses and comparison of these loss functions are presented in different GAN architectures. Finally, several suggestions on choosing suitable loss functions for image synthesis tasks are given.