Kavli Affiliate: Xiang Zhang
| First 5 Authors: Haowen Wang, Haowen Wang, , ,
| Summary:
In this paper, we address a key scientific problem in machine learning: Given
a training set for an image classification task, can we train a generative
model on this dataset to enhance the classification performance? (i.e.,
closed-set generative data augmentation). We start by exploring the
distinctions and similarities between real images and closed-set synthetic
images generated by advanced generative models. Through extensive experiments,
we offer systematic insights into the effective use of closed-set synthetic
data for augmentation. Notably, we empirically determine the equivalent scale
of synthetic images needed for augmentation. In addition, we also show
quantitative equivalence between the real data augmentation and open-set
generative augmentation (generative models trained using data beyond the given
training set). While it aligns with the common intuition that real images are
generally preferred, our empirical formulation also offers a guideline to
quantify the increased scale of synthetic data augmentation required to achieve
comparable image classification performance. Our results on natural and medical
image datasets further illustrate how this effect varies with the baseline
training set size and the amount of synthetic data incorporated.
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