Methods › Computer Vision › Image Data Augmentation › AutoAugment
AutoAugment
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
AutoAugment is an automated approach to find data augmentation policies from data. It formulates the problem of finding the best augmentation policy as a discrete search problem. It consists of two components: a search algorithm and a search space.
At a high level, the search algorithm (implemented as a controller RNN) samples a data augmentation policy S, which has information about what image processing operation to use, the probability of using the operation in each batch, and the magnitude of the operation. The policy S is used to train a neural network with a fixed architecture, whose validation accuracy R is sent back to update the controller. Since R is not differentiable, the controller will be updated by policy gradient methods.
The operations used are from PIL, a popular Python image library: all functions in PIL that accept an image as input and output an image. It additionally uses two other augmentation techniques: Cutout and SamplePairing. The operations searched over are ShearX/Y, TranslateX/Y, Rotate, AutoContrast, Invert, Equalize, Solarize, Posterize, Contrast, Color, Brightness, Sharpness, Cutout and Sample Pairing.
Papers archive 2025-07-28
30 shown of 60, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.
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Data Augmentation For Small Object using Fast AutoAugment 10 Jun 2025 · 0 repositories · arXiv:2506.08956
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Learning Tree-Structured Composition of Data Augmentation 26 Aug 2024 · 1 repository · arXiv:2408.14381
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SegSTRONG-C: Segmenting Surgical Tools Robustly On Non-adversarial Generated Corruptions -- An EndoVis'24 Challenge 16 Jul 2024 · 1 repository · arXiv:2407.11906
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AutoAugment Is What You Need: Enhancing Rule-based Augmentation Methods in Low-resource Regimes 8 Feb 2024 · 1 repository · arXiv:2402.05584
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AutoAugment Input Transformation for Highly Transferable Targeted Attacks 21 Dec 2023 · 0 repositories · arXiv:2312.14218
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LA3: Efficient Label-Aware AutoAugment 20 Apr 2023 · 1 repository · arXiv:2304.10310
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RangeAugment: Efficient Online Augmentation with Range Learning 20 Dec 2022 · 1 repository · arXiv:2212.10553
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Efficient and Effective Augmentation Strategy for Adversarial Training 27 Oct 2022 · 2 repositories · arXiv:2210.15318Syntology ran 2 of 7 samples · 5 unverified
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Learning Self-Regularized Adversarial Views for Self-Supervised Vision Transformers 16 Oct 2022 · 1 repository · arXiv:2210.08458
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Ultrafast Image Categorization in Biology and Neural Models 7 May 2022 · 1 repository · arXiv:2205.03635
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Deep AutoAugment 11 Mar 2022 · 1 repository · arXiv:2203.06172
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SelectAugment: Hierarchical Deterministic Sample Selection for Data Augmentation 6 Dec 2021 · 0 repositories · arXiv:2112.02862
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Challenges of Adversarial Image Augmentations 24 Nov 2021 · 0 repositories · arXiv:2111.12427
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Learning Augmentation Distributions using Transformed Risk Minimization 16 Nov 2021 · 0 repositories · arXiv:2111.08190
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Improving Model Generalization by Agreement of Learned Representations from Data Augmentation 20 Oct 2021 · 1 repository · arXiv:2110.10536
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Text AutoAugment: Learning Compositional Augmentation Policy for Text Classification 1 Sep 2021 · 1 repository · arXiv:2109.00523
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Direct Differentiable Augmentation Search 9 Apr 2021 · 1 repository · arXiv:2104.04282
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Relating Adversarially Robust Generalization to Flat Minima 9 Apr 2021 · 0 repositories · arXiv:2104.04448
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Robust and Accurate Object Detection via Adversarial Learning 23 Mar 2021 · 3 repositories · arXiv:2103.13886
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Local Patch AutoAugment with Multi-Agent Collaboration 20 Mar 2021 · 2 repositories · arXiv:2103.11099
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AutoDO: Robust AutoAugment for Biased Data with Label Noise via Scalable Probabilistic Implicit Differentiation 10 Mar 2021 · 1 repository · arXiv:2103.05863Syntology ran 5 of 7 samples · 2 unverified · 7 pointer-only (licence)
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Faster and Smarter AutoAugment: Augmentation Policy Search Based on Dynamic Data-Clustering 1 Jan 2021 · 0 repositories
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NOSE Augment: Fast and Effective Data Augmentation Without Searching 1 Jan 2021 · 0 repositories
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Semantic Aware Data Augmentation for Cell Nuclei Microscopical Images With Artificial Neural Networks 1 Jan 2021 · 0 repositories
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StackMix: A complementary Mix algorithm 25 Nov 2020 · 0 repositories · arXiv:2011.12618
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KeepAugment: A Simple Information-Preserving Data Augmentation Approach 23 Nov 2020 · 1 repository · arXiv:2011.11778
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An Efficient and Scalable Deep Learning Approach for Road Damage Detection 18 Nov 2020 · 2 repositories · arXiv:2011.09577
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Regularizing Neural Networks via Adversarial Model Perturbation 10 Oct 2020 · 1 repository · arXiv:2010.04925Syntology ran 0 of 12 samples · 12 unverified
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A Technical Report for VIPriors Image Classification Challenge 17 Jul 2020 · 0 repositories · arXiv:2007.08722
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Hypernetwork-Based Augmentation 11 Jun 2020 · 0 repositories · arXiv:2006.06320
Tasks archive 2025-07-28
20 shown of 68 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.
Usage over time archive 2025-07-28
Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).
Categories archive 2025-07-28
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections