Methods › Computer Vision › Image Representations › Laplacian Pyramid › Papers where code ran, page 1
Laplacian Pyramid
Papers archive 2025-07-28
archive papers tagged: 31 · with a code link: 13 · where Syntology ran a sample: 4 (4 with a run with no instrument failure, 0 where every run was a failure of Syntology's instrument) Syntology
Show: all tagged papersonly where code ran (4 of 31 tagged: 4 with a run with no instrument failure, 0 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 4 of the 4 tagged papers where Syntology ran at least one harvested sample (4 with a run with no instrument failure, 0 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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Likelihood Training of Cascaded Diffusion Models via Hierarchical Volume-preserving Maps 13 Jan 2025 · 1 repository · arXiv:2501.06999Syntology official (archive's flag): 7 ran · 7 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; 3 where Syntology's instrument failed) · 3 unverified (of 10 harvested samples)
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SHMT: Self-supervised Hierarchical Makeup Transfer via Latent Diffusion Models 15 Dec 2024 · 2 repositories · arXiv:2412.11058Syntology 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, 3 violated, 2 with no contract checked; 3 where Syntology's instrument failed) · 2 unverified (of 10 harvested samples) · 10 pointer-only (licence)
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MFDNet: Multi-Frequency Deflare Network for Efficient Nighttime Flare Removal 26 Jun 2024 · 1 repository · arXiv:2406.18079Syntology 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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Revisiting Image Pyramid Structure for High Resolution Salient Object Detection 20 Sep 2022 · 3 repositories · arXiv:2209.09475Syntology official (archive's flag): 8 ran · 10 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; 3 where Syntology's instrument failed) · 1 unverified (of 11 harvested samples)