SOTAVerified

Learning Unsupervised Cross-domain Image-to-Image Translation Using a Shared Discriminator

2021-02-09Code Available0· sign in to hype

Rajiv Kumar, Rishabh Dabral, G. Sivakumar

Code Available — Be the first to reproduce this paper.

Reproduce

Code

Abstract

Unsupervised image-to-image translation is used to transform images from a source domain to generate images in a target domain without using source-target image pairs. Promising results have been obtained for this problem in an adversarial setting using two independent GANs and attention mechanisms. We propose a new method that uses a single shared discriminator between the two GANs, which improves the overall efficacy. We assess the qualitative and quantitative results on image transfiguration, a cross-domain translation task, in a setting where the target domain shares similar semantics to the source domain. Our results indicate that even without adding attention mechanisms, our method performs at par with attention-based methods and generates images of comparable quality.

Tasks

Benchmark Results

DatasetModelMetricClaimedVerifiedStatus
Apples and OrangesShared discriminator GANKernel Inception Distance4.4Unverified
Zebra and HorsesShared discriminator GANKernel Inception Distance5.8Unverified

Reproductions