AFF-UNIT: Adaptive feature fusion for unsupervised image-to-image translation

The task of image-to-image translation is to generate images closer to the target domain style while preserving the significant features of the original image. This paper contends an adaptive feature fusion method for unsupervised image translation. The proposed architecture, termed as AFF-UNIT, is based on a compact network structure to further improve the quality of generated images. First of all, a feature extraction module based on an adaptive feature fusion method is proposed, which combines low-level fine-grained information and high-level semantic information to obtain feature maps with richer information. At the same time, a feature-similarity loss is proposed to guide the feature extraction module to extract features that are more conducive to improving the translation result. In addition, AFF-UNIT reuses the feature extraction module in the generator and discriminator to simplify the framework. Extensive experiments on five popular benchmarks demonstrate the superior performance of AFF-UNIT over state-of-the-art methods in terms of FID, KID, IS, and also human preference. Comprehensive ablation studies are also carried out to isolate the validity of each proposed component.