- 글번호
- 24242156
- 일 자
- 19.06.04
- 조회수
- 450
- 글쓴이
- ims
- [2019.05.28 특강] Fast AutoAugment
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연 사 : 임성빈 (카카오브레인)
제 목 : Fast AutoAugment
일 시 : 2018년 5월 28일 화요일 오후 2시
장 소 : 헬렌관 401-1호
초 록: 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.
