  Image translation between two domains is a class of problems aiming to learn
mapping from an input image in the source domain to an output image in the
target domain. It has been applied to numerous domains, such as data
augmentation, domain adaptation, and unsupervised training. When paired
training data is not accessible, image translation becomes an ill-posed
problem. We constrain the problem with the assumption that the translated image
needs to be perceptually similar to the original image and also appears to be
drawn from the new domain, and propose a simple yet effective image translation
model consisting of a single generator trained with a self-regularization term
and an adversarial term. We further notice that existing image translation
techniques are agnostic to the subjects of interest and often introduce
unwanted changes or artifacts to the input. Thus we propose to add an attention
module to predict an attention map to guide the image translation process. The
module learns to attend to key parts of the image while keeping everything else
unaltered, essentially avoiding undesired artifacts or changes. The predicted
attention map also opens door to applications such as unsupervised segmentation
and saliency detection. Extensive experiments and evaluations show that our
model while being simpler, achieves significantly better performance than
existing image translation methods.
