Methods › Computer Vision › Image Data Augmentation › CutMix › Papers, page 3
CutMix
Papers archive 2025-07-28
archive papers tagged: 208 · with a code link: 85 · where Syntology ran a sample: 18 (15 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 (18 of 208 tagged: 15 with a run with no instrument failure, 3 where every run was a failure of Syntology's instrument)
Page 3 of 3: papers 201 to 208 of 208, newest first by the archive's date (ties by slug), in archive order.
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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FMix: Enhancing Mixed Sample Data Augmentation 27 Feb 2020 · 5 repositories · arXiv:2002.12047Syntology official (archive's flag): 5 ran · 8 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; 7 where Syntology's instrument failed) · 0 unverified (of 8 harvested samples) · 3 pointer-only (licence)
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On Feature Normalization and Data Augmentation 25 Feb 2020 · 1 repository · arXiv:2002.11102Syntology official (archive's flag): 6 ran · 6 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; 3 where Syntology's instrument failed) · 8 unverified (of 14 harvested samples) · 2 pointer-only (licence)
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MaxUp: A Simple Way to Improve Generalization of Neural Network Training 20 Feb 2020 · 1 repository · arXiv:2002.09024Syntology 0 ran · 1 unverified (of 1 harvested sample)
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Efficient Model for Image Classification With Regularization Tricks 1 Feb 2020 · 1 repository
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Structured Consistency Loss for semi-supervised semantic segmentation 14 Jan 2020 · 0 repositories · arXiv:2001.04647
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Semi-supervised semantic segmentation needs strong, varied perturbations 5 Jun 2019 · 5 repositories · arXiv:1906.01916Syntology official (archive's flag): 1 ran · 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) · 2 unverified (of 3 harvested samples)
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CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features 13 May 2019 · 30 repositories · arXiv:1905.04899Syntology community repositories only · 17 ran (of which 6 constructed an object rather than computing a result; 11 with no instrument failure: 2 honoured, 0 violated, 9 with no contract checked; 6 where Syntology's instrument failed) · 7 unverified (of 24 harvested samples) · 5 pointer-only (licence)
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From Facial Expression Recognition to Interpersonal Relation Prediction 21 Sep 2016 · 0 repositories · arXiv:1609.06426