Methods › Computer Vision › Generative Models › Denoising Autoencoder › Papers where code ran, page 1
Denoising Autoencoder
Papers archive 2025-07-28
archive papers tagged: 182 · with a code link: 70 · where Syntology ran a sample: 8 (7 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 (8 of 182 tagged: 7 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 8 of the 8 tagged papers where Syntology ran at least one harvested sample (7 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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Diffusion Models Learn Low-Dimensional Distributions via Subspace Clustering 4 Sep 2024 · 1 repository · arXiv:2409.02426Syntology 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) · 1 unverified (of 4 harvested samples) · 4 pointer-only (licence)
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Deconstructing Denoising Diffusion Models for Self-Supervised Learning 25 Jan 2024 · 1 repository · arXiv:2401.14404Syntology 13 ran (of which 0 constructed an object rather than computing a result; 12 with no instrument failure: 4 honoured, 0 violated, 8 with no contract checked; 1 where Syntology's instrument failed) · 2 unverified (of 15 harvested samples) · 15 pointer-only (licence)
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Unleashing Text-to-Image Diffusion Models for Visual Perception 3 Mar 2023 · 2 repositories · arXiv:2303.02153Syntology official (archive's flag): 5 ran · 5 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 2 violated, 2 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified (of 5 harvested samples) · 3 pointer-only (licence)
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SinIR: Efficient General Image Manipulation with Single Image Reconstruction 14 Jun 2021 · 1 repository · arXiv:2106.07140Syntology official (archive's flag): 3 ran · 3 ran (of which 2 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) · 0 unverified (of 3 harvested samples) · 3 pointer-only (licence)
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AR-DAE: Towards Unbiased Neural Entropy Gradient Estimation 9 Jun 2020 · 2 repositories · arXiv:2006.05164Syntology 13 ran (of which 2 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 9 where Syntology's instrument failed) · 0 unverified (of 13 harvested samples) · 13 pointer-only (licence)
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BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension 29 Oct 2019 · 47 repositories · arXiv:1910.13461Syntology 40 ran (of which 7 constructed an object rather than computing a result; 31 with no instrument failure: 1 honoured, 1 violated, 29 with no contract checked; 9 where Syntology's instrument failed) · 13 unverified (of 53 harvested samples) · 13 pointer-only (licence)
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Generating Classification Weights with GNN Denoising Autoencoders for Few-Shot Learning 3 May 2019 · 1 repository · arXiv:1905.01102Syntology official (archive's flag): 7 ran · 7 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified (of 8 harvested samples) · 1 pointer-only (licence)
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Implicit 3D Orientation Learning for 6D Object Detection from RGB Images 4 Feb 2019 · 1 repository · arXiv:1902.01275Syntology 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) · 4 unverified (of 5 harvested samples)