Methods › Computer Vision › Image Data Augmentation › AutoAugment › Papers where code ran, page 1
AutoAugment
Papers archive 2025-07-28
archive papers tagged: 60 · with a code link: 45 · where Syntology ran a sample: 20 (17 with a run with no instrument failure, 3 where every run was a failure of Syntology's instrument) Syntology
Show: all tagged papersonly where code ran (20 of 60 tagged: 17 with a run with no instrument failure, 3 where every run was a failure of Syntology's instrument)
Syntology We ran code from the paper's repository; we did not isolate this method inside it.
Page 1 of 1: papers 1 to 20 of the 20 tagged papers where Syntology ran at least one harvested sample (17 with a run with no instrument failure, 3 where every run was a failure of Syntology's instrument), newest first by the archive's date (ties by arXiv id). This is a filter on Syntology's record ordered by date only, not a ranking; a run is not a correctness claim. A paper missing from this list is not a recorded non-run: it may have no arXiv id, no harvested code, or only samples that have not run yet.
Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code, as “N ran (of which C constructed an object rather than computing a result; K with no instrument failure: H honoured, V violated, P with no contract checked; I where Syntology's instrument failed) · U unverified”; the instrument figure counts failures of Syntology's instrument, not of the code. It is per sample and not a correctness claim. When the archive marks a repository official for the paper, the line starts with that repository's state (the archive's flag, not a verdict on who wrote the code; “community repositories only” when every sample that ran came from a community repository, “official: no sample here; runs from other or unrecorded repositories” when some came from a repository the paper names or has in its text, or from none recorded); hover it for the repositories the samples that ran came from.
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Efficient and Effective Augmentation Strategy for Adversarial Training 27 Oct 2022 · 2 repositories · arXiv:2210.15318Syntology official (archive's flag): 7 ran · 7 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified (of 7 harvested samples) · 2 pointer-only (licence)
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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 official (archive's flag): 6 ran · 6 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 2 where Syntology's instrument failed) · 1 unverified (of 7 harvested samples) · 7 pointer-only (licence)
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Regularizing Neural Networks via Adversarial Model Perturbation 10 Oct 2020 · 1 repository · arXiv:2010.04925Syntology official (archive's flag): 8 ran · 8 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 0 honoured, 0 violated, 8 with no contract checked; 0 where Syntology's instrument failed) · 4 unverified (of 12 harvested samples)
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AutoCLINT: The Winning Method in AutoCV Challenge 2019 9 May 2020 · 1 repository · arXiv:2005.04373Syntology official (archive's flag): 3 ran · 3 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 3 where Syntology's instrument failed) · 0 unverified (of 3 harvested samples)
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On the Generalization Effects of Linear Transformations in Data Augmentation 2 May 2020 · 2 repositories · arXiv:2005.00695Syntology official (archive's flag): 5 ran · 6 ran (of which 1 constructed an object rather than computing a result; 6 with no instrument failure: 1 honoured, 0 violated, 5 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified (of 9 harvested samples)
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Supervised Contrastive Learning 23 Apr 2020 · 26 repositories · arXiv:2004.11362Syntology community repositories only · 17 ran (of which 0 constructed an object rather than computing a result; 14 with no instrument failure: 2 honoured, 0 violated, 12 with no contract checked; 3 where Syntology's instrument failed) · 6 unverified (of 23 harvested samples) · 5 pointer-only (licence)
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ResNeSt: Split-Attention Networks 19 Apr 2020 · 36 repositories · arXiv:2004.08955Syntology official (archive's flag): 5 ran · 28 ran (of which 0 constructed an object rather than computing a result; 25 with no instrument failure: 0 honoured, 0 violated, 25 with no contract checked; 3 where Syntology's instrument failed) · 20 unverified (of 48 harvested samples) · 23 pointer-only (licence)
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SuperMix: Supervising the Mixing Data Augmentation 10 Mar 2020 · 2 repositories · arXiv:2003.05034Syntology official (archive's flag): 4 ran · 4 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 3 where Syntology's instrument failed) · 0 unverified (of 4 harvested samples) · 4 pointer-only (licence)
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DADA: Differentiable Automatic Data Augmentation 8 Mar 2020 · 1 repository · arXiv:2003.03780Syntology official (archive's flag): 8 ran · 8 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 3 where Syntology's instrument failed) · 1 unverified (of 9 harvested samples) · 5 pointer-only (licence)
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Compounding the Performance Improvements of Assembled Techniques in a Convolutional Neural Network 17 Jan 2020 · 1 repository · arXiv:2001.06268Syntology official (archive's flag): 3 ran · 3 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 1 where Syntology's instrument failed) · 8 unverified (of 11 harvested samples)
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Adversarial Examples Improve Image Recognition 21 Nov 2019 · 6 repositories · arXiv:1911.09665Syntology community repositories only · 2 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified (of 2 harvested samples)
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ReMixMatch: Semi-Supervised Learning with Distribution Alignment and Augmentation Anchoring 21 Nov 2019 · 3 repositories · arXiv:1911.09785Syntology official: harvested, nothing ran · 1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 3 unverified (of 4 harvested samples) · 1 pointer-only (licence)
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Faster AutoAugment: Learning Augmentation Strategies using Backpropagation 16 Nov 2019 · 1 repository · arXiv:1911.06987Syntology 6 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified (of 9 harvested samples)
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RandAugment: Practical automated data augmentation with a reduced search space 30 Sep 2019 · 19 repositories · arXiv:1909.13719Syntology 58 ran (of which 1 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 1 violated, 6 with no contract checked; 51 where Syntology's instrument failed) · 7 unverified (of 65 harvested samples) · 17 pointer-only (licence)
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Improving Robustness Without Sacrificing Accuracy with Patch Gaussian Augmentation 6 Jun 2019 · 2 repositories · arXiv:1906.02611Syntology 1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified (of 1 harvested sample)
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EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks 28 May 2019 · 144 repositories · arXiv:1905.11946Syntology official: no sample here; runs from other or unrecorded repositories · 198 ran (of which 73 constructed an object rather than computing a result; 157 with no instrument failure: 26 honoured, 2 violated, 129 with no contract checked; 41 where Syntology's instrument failed) · 104 unverified (of 302 harvested samples) · 113 pointer-only (licence)
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Population Based Augmentation: Efficient Learning of Augmentation Policy Schedules 14 May 2019 · 3 repositories · arXiv:1905.05393Syntology official (archive's flag): 11 ran · 11 ran (of which 0 constructed an object rather than computing a result; 11 with no instrument failure: 0 honoured, 0 violated, 11 with no contract checked; 0 where Syntology's instrument failed) · 7 unverified (of 18 harvested samples) · 18 pointer-only (licence)
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Fast AutoAugment 1 May 2019 · 11 repositories · arXiv:1905.00397Syntology official (archive's flag): 9 ran · 37 ran (of which 2 constructed an object rather than computing a result; 21 with no instrument failure: 0 honoured, 0 violated, 21 with no contract checked; 16 where Syntology's instrument failed) · 3 unverified (of 40 harvested samples) · 7 pointer-only (licence)
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MultiGrain: a unified image embedding for classes and instances 14 Feb 2019 · 3 repositories · arXiv:1902.05509Syntology community repositories only · 4 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified (of 4 harvested samples)
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AutoAugment: Learning Augmentation Policies from Data 24 May 2018 · 33 repositories · arXiv:1805.09501Syntology community repositories only · 25 ran (of which 0 constructed an object rather than computing a result; 22 with no instrument failure: 0 honoured, 2 violated, 20 with no contract checked; 3 where Syntology's instrument failed) · 18 unverified (of 43 harvested samples) · 4 pointer-only (licence)