Methods › General › Semi-Supervised Learning Methods › MoCo v2 › Papers where code ran, page 1
MoCo v2
Papers archive 2025-07-28
archive papers tagged: 30 · with a code link: 20 · where Syntology ran a sample: 9 (8 with a run with no instrument failure, 1 where every run was a failure of Syntology's instrument) Syntology
Show: all tagged papersonly where code ran (9 of 30 tagged: 8 with a run with no instrument failure, 1 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 9 of the 9 tagged papers where Syntology ran at least one harvested sample (8 with a run with no instrument failure, 1 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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Matrix Information Theory for Self-Supervised Learning 27 May 2023 · 3 repositories · arXiv:2305.17326Syntology official (archive's flag): 3 ran · 5 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; 4 where Syntology's instrument failed) · 0 unverified (of 5 harvested samples) · 5 pointer-only (licence)
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Revisiting the Critical Factors of Augmentation-Invariant Representation Learning 30 Jul 2022 · 1 repository · arXiv:2208.00275Syntology official (archive's flag): 4 ran · 4 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; 2 where Syntology's instrument failed) · 1 unverified (of 5 harvested samples)
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Dual Temperature Helps Contrastive Learning Without Many Negative Samples: Towards Understanding and Simplifying MoCo 30 Mar 2022 · 2 repositories · arXiv:2203.17248Syntology community repositories only · 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) · 0 unverified (of 1 harvested sample) · 1 pointer-only (licence)
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DATA: Domain-Aware and Task-Aware Self-supervised Learning 17 Mar 2022 · 1 repository · arXiv:2203.09041Syntology 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) · 1 unverified (of 2 harvested samples)
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Energy-Based Contrastive Learning of Visual Representations 10 Feb 2022 · 1 repository · arXiv:2202.04933Syntology official (archive's flag): 12 ran · 12 ran (of which 2 constructed an object rather than computing a result; 5 with no instrument failure: 1 honoured, 0 violated, 4 with no contract checked; 7 where Syntology's instrument failed) · 2 unverified (of 14 harvested samples) · 14 pointer-only (licence)
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SEED: Self-supervised Distillation For Visual Representation 12 Jan 2021 · 1 repository · arXiv:2101.04731Syntology official (archive's flag): 1 ran · 1 ran (of which 1 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; the one sample that ran constructed an object rather than computing a result (of 1 harvested sample) · 1 pointer-only (licence)
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How Well Do Self-Supervised Models Transfer? 26 Nov 2020 · 1 repository · arXiv:2011.13377Syntology 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) · 1 unverified (of 5 harvested samples)
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Unsupervised Feature Learning by Cross-Level Instance-Group Discrimination 9 Aug 2020 · 2 repositories · arXiv:2008.03813Syntology 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) · 2 unverified (of 8 harvested samples) · 1 pointer-only (licence)
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Improved Baselines with Momentum Contrastive Learning 9 Mar 2020 · 36 repositories · arXiv:2003.04297Syntology official: no sample here; runs from other or unrecorded repositories · 28 ran (of which 0 constructed an object rather than computing a result; 21 with no instrument failure: 0 honoured, 1 violated, 20 with no contract checked; 7 where Syntology's instrument failed) · 15 unverified (of 43 harvested samples) · 15 pointer-only (licence)