Masked Discriminators for Content-Consistent Unpaired Image-to-Image Translation

TL;DR

This paper introduces a novel approach for unpaired image-to-image translation that uses masked discriminators and a similarity sampling strategy to improve content consistency and reduce artifacts, achieving state-of-the-art results.

Contribution

The paper proposes masked discriminators with a content-based masking strategy, a local discriminator with similarity sampling, and feature-attentive denormalization for improved content preservation.

Findings

Achieves state-of-the-art results in photorealistic translation tasks.

Reduces content inconsistencies significantly.

Introduces the cKVD metric for class-level translation quality assessment.

Abstract

A common goal of unpaired image-to-image translation is to preserve content consistency between source images and translated images while mimicking the style of the target domain. Due to biases between the datasets of both domains, many methods suffer from inconsistencies caused by the translation process. Most approaches introduced to mitigate these inconsistencies do not constrain the discriminator, leading to an even more ill-posed training setup. Moreover, none of these approaches is designed for larger crop sizes. In this work, we show that masking the inputs of a global discriminator for both domains with a content-based mask is sufficient to reduce content inconsistencies significantly. However, this strategy leads to artifacts that can be traced back to the masking process. To reduce these artifacts, we introduce a local discriminator that operates on pairs of small crops selected with a similarity sampling strategy. Furthermore, we apply this sampling strategy to sample global input crops from the source and target dataset. In addition, we propose feature-attentive denormalization to selectively incorporate content-based statistics into the generator stream. In our experiments, we show that our method achieves state-of-the-art performance in photorealistic sim-to-real translation and weather translation and also performs well in day-to-night translation. Additionally, we propose the cKVD metric, which builds on the sKVD metric and enables the examination of translation quality at the class or category level.