Papers › Faster AutoAugment: Learning Augmentation Strategies using Backpropagation
Faster AutoAugment: Learning Augmentation Strategies using Backpropagation
Ryuichiro Hataya, Jan Zdenek, Kazuki Yoshizoe, Hideki Nakayama
Data augmentation methods are indispensable heuristics to boost the performance of deep neural networks, especially in image recognition tasks. Recently, several studies have shown that augmentation strategies found by search algorithms outperform hand-made strategies. Such methods employ black-box search algorithms over image transformations with continuous or discrete parameters and require a long time to obtain better strategies. In this paper, we propose a differentiable policy search pipeline for data augmentation, which is much faster than previous methods. We introduce approximate gradients for several transformation operations with discrete parameters as well as the differentiable mechanism for selecting operations. As the objective of training, we minimize the distance between the distributions of augmented data and the original data, which can be differentiated. We show that our method, Faster AutoAugment, achieves significantly faster searching than prior work without a performance drop.
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Code
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Code Syntology ran Syntology
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Data Augmentation | CIFAR-10 | Shake-Shake (26 2×96d) (Faster AA) | Percentage error | 2 | #1 of 5 | Archive leaderboard | report |
| Data Augmentation | CIFAR-10 | Shake-Shake (26 2×112d) (Faster AA) | Percentage error | 2 | #2 of 5 | Archive leaderboard | report |
| Data Augmentation | CIFAR-10 | WideResNet-28-10 (Faster AA) | Percentage error | 2.6 | #3 of 5 | Archive leaderboard | report |
| Data Augmentation | CIFAR-10 | Shake-Shake (26 2×32d) (Faster AA) | Percentage error | 2.7 | #4 of 5 | Archive leaderboard | report |
| Data Augmentation | CIFAR-10 | WideResNet-40-2 (Faster AA) | Percentage error | 3.7 | #5 of 5 | Archive leaderboard | report |
| Data Augmentation | ImageNet | ResNet-50 (Faster AA) | Accuracy (%) | 76.5 | #16 of 17 | 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
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