Adaptive data augmentation for image classification

TLDR

Data augmentation generates transformed training samples to improve classifier accuracy and robustness. This work introduces an automatic, adaptive algorithm that selects optimal transformations for data augmentation. The algorithm seeks a minimal transformation that maximizes classification loss per sample, using trust‑region optimization via linear programs, and is incorporated into SGD for training deep neural networks. Experiments on two datasets demonstrate that the method surpasses random augmentation in accuracy and robustness, and matches or exceeds existing selective sampling techniques.

Abstract

Data augmentation is the process of generating samples by transforming training data, with the target of improving the accuracy and robustness of classifiers. In this paper, we propose a new automatic and adaptive algorithm for choosing the transformations of the samples used in data augmentation. Specifically, for each sample, our main idea is to seek a small transformation that yields maximal classification loss on the transformed sample. We employ a trust-region optimization strategy, which consists of solving a sequence of linear programs. Our data augmentation scheme is then integrated into a Stochastic Gradient Descent algorithm for training deep neural networks. We perform experiments on two datasets, and show that that the proposed scheme outperforms random data augmentation algorithms in terms of accuracy and robustness, while yielding comparable or superior results with respect to existing selective sampling approaches.