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Generative Adversarial Networks (GANs) typically suffer from overfitting whenlimited training data is available. To facilitate GAN training, current methodspropose to use data-specific augmentation techniques. Despite theeffectiveness, it is difficult for these methods to scale to practicalapplications. In this work, we present ScoreMix, a novel and scalable dataaugmentation approach for various image synthesis tasks. We first produceaugmented samples using the convex combinations of the real samples. Then, weoptimize the augmented samples by minimizing the norms of the data scores,i.e., the gradients of the log-density functions. This procedure enforces theaugmented samples close to the data manifold. To estimate the scores, we traina deep estimation network with multi-scale score matching. For different imagesynthesis tasks, we train the score estimation network using different data. Wedo not require the tuning of the hyperparameters or modifications to thenetwork architecture. The ScoreMix method effectively increases the diversityof data and reduces the overfitting problem. Moreover, it can be easilyincorporated into existing GAN models with minor modifications. Experimentalresults on numerous tasks demonstrate that GAN models equipped with theScoreMix method achieve significant improvements.

ScoreMix: A Scalable Augmentation Strategy for Training GANs with Limited Data