Image-to-image (I2I) translation methods based on generative adversarial networks (GANs) typically suffer from overfitting when limited training data is available. In this work, we propose a data augmentation method (ReMix) to tackle this issue. We interpolate training samples at the feature level and propose a novel content loss based on the perceptual relations among samples. The generator learns to translate the in-between samples rather than memorizing the training set, and thereby forces the discriminator to generalize. The proposed approach effectively reduces the ambiguity of generation and renders content-preserving results. The ReMix method can be easily incorporated into existing GAN models with minor modifications. Experimental results on numerous tasks demonstrate that GAN models equipped with the ReMix method achieve significant improvements.

本文针对生成对抗网络的图像到图像转换领域中，数据有限导致过拟合的问题，提出了一种数据增强方法（ReMix）。该方法通过对特征进行插值，并结合基于感性关系的内容损失函数，实现了通过合成训练数据来强制鉴别器泛化以降低过拟合的目的。在多项实验中，GAN模型结合 ReMix 方法的性能得到了显著的提升。

ReMix: 有限数据下的图像到图像翻译