For unpaired image-to-image translation tasks, GAN-based approaches are susceptible to semantic flipping, i.e., contents are not preserved consistently. We argue that this is due to (1) the difference in semantic statistics between source and target domains and (2) the learned generators being non-robust. In this paper, we proposed a novel approach, Lipschitz regularized CycleGAN, for improving semantic robustness and thus alleviating the semantic flipping issue. During training, we add a gradient penalty loss to the generators, which encourages semantically consistent transformations. We evaluate our approach on multiple common datasets and compare with several existing GAN-based methods. Both quantitative and visual results suggest the effectiveness and advantage of our approach in producing robust transformations with fewer semantic flipping.
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