Sample Efficiency of Data Augmentation Consistency Regularization

• URL: http://arxiv.org/abs/2202.12230v1
• Date: Thu, 24 Feb 2022 17:50:31 GMT
• Title: Sample Efficiency of Data Augmentation Consistency Regularization
• Authors: Shuo Yang, Yijun Dong, Rachel Ward, Inderjit S. Dhillon, Sujay Sanghavi, Qi Lei
• Abstract summary: We first present a simple and novel analysis for linear regression, demonstrating that data augmentation consistency (DAC) is intrinsically more efficient than empirical risk minimization on augmented data (DA-ERM) We then propose a new theoretical framework for analyzing DAC, which reframes DAC as a way to reduce function class complexity.
• Score: 44.19833682906076
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Data augmentation is popular in the training of large neural networks; currently, however, there is no clear theoretical comparison between different algorithmic choices on how to use augmented data. In this paper, we take a step in this direction - we first present a simple and novel analysis for linear regression, demonstrating that data augmentation consistency (DAC) is intrinsically more efficient than empirical risk minimization on augmented data (DA-ERM). We then propose a new theoretical framework for analyzing DAC, which reframes DAC as a way to reduce function class complexity. The new framework characterizes the sample efficiency of DAC for various non-linear models (e.g., neural networks). Further, we perform experiments that make a clean and apples-to-apples comparison (i.e., with no extra modeling or data tweaks) between ERM and consistency regularization using CIFAR-100 and WideResNet; these together demonstrate the superior efficacy of DAC.

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