  Contrastive learning shows great potential in unpaired image-to-image
translation, but sometimes the translated results are in poor quality and the
contents are not preserved consistently. In this paper, we uncover that the
negative examples play a critical role in the performance of contrastive
learning for image translation. The negative examples in previous methods are
randomly sampled from the patches of different positions in the source image,
which are not effective to push the positive examples close to the query
examples. To address this issue, we present instance-wise hard Negative Example
Generation for Contrastive learning in Unpaired image-to-image Translation
(NEGCUT). Specifically, we train a generator to produce negative examples
online. The generator is novel from two perspectives: 1) it is instance-wise
which means that the generated examples are based on the input image, and 2) it
can generate hard negative examples since it is trained with an adversarial
loss. With the generator, the performance of unpaired image-to-image
translation is significantly improved. Experiments on three benchmark datasets
demonstrate that the proposed NEGCUT framework achieves state-of-the-art
performance compared to previous methods.
