Adaptive data augmentation for image classification

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.


Published in:
2016 Ieee International Conference On Image Processing (Icip), 3688-3692
Presented at:
23rd IEEE International Conference on Image Processing (ICIP)
Year:
2016
Publisher:
New York, IEEE
ISSN:
1522-4880
ISBN:
978-1-4673-9961-6
Keywords:
Laboratories:




 Record created 2016-05-21, last modified 2018-03-17

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