  Image-to-image translation aims to learn a mapping between different groups
of visually distinguishable images. While recent methods have shown impressive
ability to change even intricate appearance of images, they still rely on
domain labels in training a model to distinguish between distinct visual
features. Such dependency on labels often significantly limits the scope of
applications since consistent and high-quality labels are expensive. Instead,
we wish to capture visual features from images themselves and apply them to
enable realistic translation without human-generated labels. To this end, we
propose an unsupervised image-to-image translation method based on contrastive
learning. The key idea is to learn a discriminator that differentiates between
distinctive styles and let the discriminator supervise a generator to transfer
those styles across images. During training, we randomly sample a pair of
images and train the generator to change the appearance of one towards another
while keeping the original structure. Experimental results show that our method
outperforms the leading unsupervised baselines in terms of visual quality and
translation accuracy.
