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Image-to-image (I2I) translation methods based on generative adversarialnetworks (GANs) typically suffer from overfitting when limited training data isavailable. In this work, we propose a data augmentation method (ReMix) totackle this issue. We interpolate training samples at the feature level andpropose a novel content loss based on the perceptual relations among samples.The generator learns to translate the in-between samples rather than memorizingthe training set, and thereby forces the discriminator to generalize. Theproposed approach effectively reduces the ambiguity of generation and renderscontent-preserving results. The ReMix method can be easily incorporated intoexisting GAN models with minor modifications. Experimental results on numeroustasks demonstrate that GAN models equipped with the ReMix method achievesignificant improvements.

ReMix: Towards Image-to-Image Translation with Limited Data