Papers › Fast AutoAugment
Fast AutoAugment
Sungbin Lim, Ildoo Kim, Taesup Kim, Chiheon Kim, Sungwoong Kim
Data augmentation is an essential technique for improving generalization ability of deep learning models. Recently, AutoAugment 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.
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Code
Syntology Ran 22 of 40 code samples harvested from 6 repositories linked to this paper; 18 have no recorded run. Of those that ran: 4 ran · our draft was wrong; 12 ran · fixture could not drive it; 6 ran with no contract checked.
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Code Syntology ran Syntology
40 samples harvested; 22 ran; 0 honoured the contract we drafted; 18 have no recorded run. Read from Syntology's graph 2026-09-24; that is when this build read the record, not when the samples ran.
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Tasks
1 archive task tag without a task page not shown.
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Data Augmentation | ImageNet | ResNet-200 (Fast AA) | Accuracy (%) | 80.6 | #4 of 17 | Archive leaderboard | report |
| Data Augmentation | ImageNet | ResNet-50 (Fast AA) | Accuracy (%) | 77.6 | #13 of 17 | Archive leaderboard | report |
| Image Classification | CIFAR-10 | PyramidNet+ShakeDrop (Fast AA) | Percentage correct | 98.3 | #46 of 265 | Archive leaderboard | report |
| Image Classification | CIFAR-100 | PyramidNet+ShakeDrop (Fast AA) | Percentage correct | 88.3 | #38 of 211 | Archive leaderboard | report |
| Image Classification | ImageNet | ResNet-200 (Fast AA) | Top 1 Accuracy | 80.6% | #691 of 1060 | Archive leaderboard | report |
| Image Classification | ImageNet | ResNet-50 (Fast AA) | Top 1 Accuracy | 77.6% | #867 of 1060 | Archive leaderboard | report |
| Image Classification | SVHN | Wide-ResNet-28-10 (Fast AA) | Percentage error | 1.1 | #2 of 62 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
Introduced by this paper: Fast AutoAugment
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