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For machine learning task, lacking sufficient samples mean the trained modelhas low confidence to approach the ground truth function. Until recently, afterthe generative adversarial networks (GAN) had been proposed, we see the hope ofsmall samples data augmentation (DA) with realistic fake data, and many worksvalidated the viability of GAN-based DA. Although most of the works pointed outhigher accuracy can be achieved using GAN-based DA, some researchers stressedthat the fake data generated from GAN has inherent bias, and in this paper, weexplored when the bias is so low that it cannot hurt the performance, we setexperiments to depict the bias in different GAN-based DA setting, and from theresults, we design a pipeline to inspect specific dataset isefficiently-augmentable with GAN-based DA or not. And finally, depending on ourtrial to reduce the bias, we proposed some advice to mitigate bias in GAN-basedDA application.

Exploring Bias in GAN-based Data Augmentation for Small Samples