Methods › Computer Vision › Convolutional Neural Networks › VGG-19 › Papers where code ran, page 1
Visual Geometry Group 19 Layer CNN
VGG-19
Papers archive 2025-07-28
archive papers tagged: 87 · with a code link: 21 · where Syntology ran a sample: 8 (6 with a run with no instrument failure, 2 where every run was a failure of Syntology's instrument) Syntology
Show: all tagged papersonly where code ran (8 of 87 tagged: 6 with a run with no instrument failure, 2 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 8 of the 8 tagged papers where Syntology ran at least one harvested sample (6 with a run with no instrument failure, 2 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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Label Poisoning is All You Need 29 Oct 2023 · 1 repository · arXiv:2310.18933Syntology official (archive's flag): 6 ran · 6 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; 1 where Syntology's instrument failed) · 4 unverified (of 10 harvested samples) · 1 pointer-only (licence)
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Connectivity Matters: Neural Network Pruning Through the Lens of Effective Sparsity 5 Jul 2021 · 1 repository · arXiv:2107.02306Syntology 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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DoubleU-Net: A Deep Convolutional Neural Network for Medical Image Segmentation 8 Jun 2020 · 4 repositories · arXiv:2006.04868Syntology official (archive's flag): 1 ran · 2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 1 honoured, 1 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified (of 5 harvested samples) · 5 pointer-only (licence)
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Priority-based Parameter Propagation for Distributed DNN Training 10 May 2019 · 1 repository · arXiv:1905.03960Syntology official (archive's flag): 15 ran · 15 ran (of which 0 constructed an object rather than computing a result; 15 with no instrument failure: 0 honoured, 0 violated, 15 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified (of 17 harvested samples)
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Generalisation in humans and deep neural networks 27 Aug 2018 · 2 repositories · arXiv:1808.08750Syntology official (archive's flag): 2 ran · 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) · 2 pointer-only (licence)
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DeepMVS: Learning Multi-view Stereopsis 2 Apr 2018 · 1 repository · arXiv:1804.00650Syntology official (archive's flag): 2 ran · 2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 1 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified (of 2 harvested samples)
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SqueezeNext: Hardware-Aware Neural Network Design 23 Mar 2018 · 8 repositories · arXiv:1803.10615Syntology 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)
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Very Deep Convolutional Networks for Large-Scale Image Recognition 4 Sep 2014 · 305 repositories · arXiv:1409.1556Syntology 81 ran (of which 0 constructed an object rather than computing a result; 71 with no instrument failure: 0 honoured, 0 violated, 71 with no contract checked; 10 where Syntology's instrument failed) · 41 unverified (of 122 harvested samples) · 8 pointer-only (licence)