Asymmetric GANs for Image-to-Image Translation

TL;DR

This paper introduces AsymmetricGAN, a novel GAN architecture with unequal generator sizes and strategies, improving image translation quality and stability in both supervised and unsupervised settings by addressing limitations of symmetric models.

Contribution

The paper proposes the first asymmetric GAN structure for image translation, with different generator sizes and sharing strategies, enhancing performance and stability over symmetric models.

Findings

AsymmetricGAN outperforms existing GANs on 8 datasets.

AsymmetricGAN achieves higher translation consistency.

AsymmetricGAN demonstrates better training stability.

Abstract

Existing models for unsupervised image translation with Generative Adversarial Networks (GANs) can learn the mapping from the source domain to the target domain using a cycle-consistency loss. However, these methods always adopt a symmetric network architecture to learn both forward and backward cycles. Because of the task complexity and cycle input difference between the source and target domains, the inequality in bidirectional forward-backward cycle translations is significant and the amount of information between two domains is different. In this paper, we analyze the limitation of existing symmetric GANs in asymmetric translation tasks, and propose an AsymmetricGAN model with both translation and reconstruction generators of unequal sizes and different parameter-sharing strategy to adapt to the asymmetric need in both unsupervised and supervised image translation tasks. Moreover, the training stage of existing methods has the common problem of model collapse that degrades the quality of the generated images, thus we explore different optimization losses for better training of AsymmetricGAN, making image translation with higher consistency and better stability. Extensive experiments on both supervised and unsupervised generative tasks with 8 datasets show that AsymmetricGAN achieves superior model capacity and better generation performance compared with existing GANs. To the best of our knowledge, we are the first to investigate the asymmetric GAN structure on both unsupervised and supervised image translation tasks.