TRIP: Refining Image-to-Image Translation via Rival Preferences

Yinghua Yao, Yuangang Pan, Ivor Tsang, Xin Yao

Keywords: Fine-grained image-to-image translation, GAN, relative attributes, ranker

Abstract: We propose a new model to refine image-to-image translation via an adversarial ranking process. In particular, we simultaneously train two modules: a generator that translates an input image to the desired image with smooth subtle changes with respect to some specific attributes; and a ranker that ranks rival preferences consisting of the input image and the desired image. Rival preferences refer to the adversarial ranking process: (1) the ranker thinks no difference between the desired image and the input image in terms of the desired attributes; (2) the generator fools the ranker to believe that the desired image changes the attributes over the input image as desired. Real image preferences are introduced to guide the ranker to rank image pairs regarding the interested attributes only. With an effective ranker, the generator would “win” the adversarial game by producing high-quality images that present desired changes over the attributes compared to the input image. The experiments demonstrate that our TRIP can generate high-fidelity images which exhibit smooth changes with the strength of the attributes.

One-sentence Summary: We propose TRIP consisting of a ranker and a generator for a high-quality fine-grained translation, where the rival preference is constructed to evoke the adversarial training between the ranker and the generator.

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