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Image translation between two domains is a class of problems aiming to learnmapping from an input image in the source domain to an output image in thetarget domain. It has been applied to numerous domains, such as dataaugmentation, domain adaptation, and unsupervised training. When pairedtraining data is not accessible, image translation becomes an ill-posedproblem. We constrain the problem with the assumption that the translated imageneeds to be perceptually similar to the original image and also appears to bedrawn from the new domain, and propose a simple yet effective image translationmodel consisting of a single generator trained with a self-regularization termand an adversarial term. We further notice that existing image translationtechniques are agnostic to the subjects of interest and often introduceunwanted changes or artifacts to the input. Thus we propose to add an attentionmodule to predict an attention map to guide the image translation process. Themodule learns to attend to key parts of the image while keeping everything elseunaltered, essentially avoiding undesired artifacts or changes. The predictedattention map also opens door to applications such as unsupervised segmentationand saliency detection. Extensive experiments and evaluations show that ourmodel while being simpler, achieves significantly better performance thanexisting image translation methods.

Show, Attend and Translate: Unsupervised Image Translation with Self-Regularization and Attention