Asymmetric Generative Adversarial Networks for Image-to-Image Translation

12/14/2019
by   Hao Tang, et al.
28

State-of-the-art models for unpaired image-to-image translation with Generative Adversarial Networks (GANs) can learn the mapping from the source domain to the target domain using a cycle-consistency loss. The intuition behind these models is that if we translate from one domain to the other and back again we should arrive at where we started. However, existing 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 image 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 the existing symmetric GAN models 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-to-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, and thus make image-to-image translation with higher consistency and better stability. Extensive experiments on both supervised and unsupervised generative tasks with several publicly available datasets demonstrate that the proposed AsymmetricGAN achieves superior model capacity and better generation performance compared with existing GAN models. To the best of our knowledge, we are the first to investigate the asymmetric GAN framework on both unsupervised and supervised image-to-image translation tasks. The source code, data and trained models are available at https://github.com/Ha0Tang/AsymmetricGAN.

READ FULL TEXT

page 1

page 3

page 5

page 7

page 8

page 9

page 10

page 12

research
01/14/2019

Dual Generator Generative Adversarial Networks for Multi-Domain Image-to-Image Translation

State-of-the-art methods for image-to-image translation with Generative ...
research
02/13/2018

An Optimized Architecture for Unpaired Image-to-Image Translation

Unpaired Image-to-Image translation aims to convert the image from one d...
research
12/08/2021

Adverse Weather Image Translation with Asymmetric and Uncertainty-aware GAN

Adverse weather image translation belongs to the unsupervised image-to-i...
research
12/25/2019

Asymmetric GAN for Unpaired Image-to-image Translation

Unpaired image-to-image translation problem aims to model the mapping fr...
research
03/28/2023

Rethinking CycleGAN: Improving Quality of GANs for Unpaired Image-to-Image Translation

An unpaired image-to-image (I2I) translation technique seeks to find a m...
research
03/04/2022

UVCGAN: UNet Vision Transformer cycle-consistent GAN for unpaired image-to-image translation

Image-to-image translation has broad applications in art, design, and sc...

Please sign up or login with your details

Forgot password? Click here to reset