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Fast AutoAugment
Sungbin Lim · Ildoo Kim · Taesup Kim · Chiheon Kim · Sungwoong Kim

Tue Dec 10 05:30 PM -- 07:30 PM (PST) @ East Exhibition Hall B + C #5

Data augmentation is an essential technique for improving generalization ability of deep learning models. Recently, AutoAugment \cite{cubuk2018autoaugment} has been proposed as an algorithm to automatically search for augmentation policies from a dataset and has significantly enhanced performances on many image recognition tasks. However, its search method requires thousands of GPU hours even for a relatively small dataset. In this paper, we propose an algorithm called Fast AutoAugment that finds effective augmentation policies via a more efficient search strategy based on density matching. In comparison to AutoAugment, the proposed algorithm speeds up the search time by orders of magnitude while achieves comparable performances on image recognition tasks with various models and datasets including CIFAR-10, CIFAR-100, SVHN, and ImageNet. Our code is open to the public by the official GitHub\footnote{\url{https://github.com/kakaobrain/fast-autoaugment}} of Kakao Brain.

Author Information

Sungbin Lim (Korea University)
Ildoo Kim (Kakao Brain)
Taesup Kim (Mila, Université de Montréal / Kakao Brain)
Chiheon Kim (Kakao Brain)
Sungwoong Kim (Kakao Brain)

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