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Image-to-image translation is a fundamental task in computer vision. Ittransforms images from one domain to images in another domain so that they haveparticular domain-specific characteristics. Most prior works train a generativemodel to learn the mapping from a source domain to a target domain. However,learning such mapping between domains is challenging because data fromdifferent domains can be highly unbalanced in terms of both quality andquantity. To address this problem, we propose a new approach to extract imagefeatures by learning the similarities and differences of samples within thesame data distribution via a novel contrastive learning framework, which wecall Auto-Contrastive-Encoder (ACE). ACE learns the content code as thesimilarity between samples with the same content information and differentstyle perturbations. The design of ACE enables us to achieve zero-shotimage-to-image translation with no training on image translation tasks for thefirst time.  Moreover, our learning method can learn the style features of images ondifferent domains effectively. Consequently, our model achieves competitiveresults on multimodal image translation tasks with zero-shot learning as well.Additionally, we demonstrate the potential of our method in transfer learning.With fine-tuning, the quality of translated images improves in unseen domains.Even though we use contrastive learning, all of our training can be performedon a single GPU with the batch size of 8.

ACE: Zero-Shot Image to Image Translation via Pretrained Auto-Contrastive-Encoder