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consistency_loss

Syntologyentry name in harvested coderead from the graph 2026-09-24

consistency_loss appears in the code Syntology harvested for 19 papers, as 20 distinct code bodies found in 22 places (a place is one code body under one paper). At least one of them ran in 11 of the papers; 1 of the code bodies carries a behaviour fingerprint.

What this page is not. Routines are grouped here by the exact string of their function or class name. Nothing asserts that two samples named consistency_loss do the same thing, share code, or are comparable; the name is a string, not an identity. Behaviour outputs (what a fingerprinted sample returned on the shared battery) are not in this export and are not shown here; the graph at syntology.ai holds them. "Ran" means executed on a synthesized fixture, not that the code is correct or reproduces a paper.

Samples Syntology

Syntology ran 13 of the 20 distinct code bodies named consistency_loss; 7 are unverified. One tile per status, in the site's fixed vocabulary, each code body counted once:

0ran · honoured contract
0ran · violated contract
1ran · our draft was wrong
8ran · fixture could not drive it
4ran
7unverified
1fingerprinted

Licence is a property of each copy, so it is counted per place: 9 of the 22 places are pointer only (Syntology does not serve that copy's text). This site shows no code text for any sample; every row below links to the file in its repository where the record names one.

“Ran” means the sample executed on a synthesized input; it does not mean the output is correct. “Honoured” and “violated” refer to a contract Syntology drafted from the code itself; “our draft was wrong” and “fixture could not drive it” are failures of Syntology's instrument, not of the code, and those samples did run. The ran count above is every status except unverified, the same rule as each paper page.

Papers

19 papers shown of 19, newest first; 22 places in the table. A paper with no recorded date is placed by the month its arXiv id encodes, shown in the Date column as YYYY-MM (from id). One row per place: a paper whose repository defines the name more than once appears more than once, and the same code body held for several papers appears once under each, with the same status. Titles and dates are the archive's archive 2025-07-28 for papers in the archive, and the graph's for 2 papers added by Syntology; 1 papers have no page here and are shown by arXiv id only. Status and fingerprint are Syntology's record of each code body; licence is recorded for each place. The File cell ends with the code body's code_sha256, Syntology's identity for that exact code: an agent fetches the stored sample with Syntology's MCP tool get_code(code_sha256="…") (how to connect); click an identity to copy that call.

PaperDateFileStatus SyntologyLicence
Reducing Bias and Variance: Generative Semantic Guidance and Bi-Layer Ensemble for Image Clustering added by Syntology 2026-05 (from id) 2017LI/GSEC/ensemble_en4_jian.py 6b1bfdf6fcd9c84b ran fingerprinted no licence file found · pointer only
Keep It on a Leash: Controllable Pseudo-label Generation Towards Realistic Long-Tailed Semi-Supervised Learning added by Syntology 2025-10 (from id) yaxinhou/CPG/semilearn/core/criterions/consistency.py fed286760e483b83 unverified no licence file found · pointer only
arXiv:2503.02231 2025-03 (from id) BoCheng-96/CGMatch/semilearn/core/criterions/consistency.py 2e5e904c31cc7999 unverified MIT (permissive)
(FL)$^2$: Overcoming Few Labels in Federated Semi-Supervised Learning 30 Oct 2024 seungjoo-ai/FLFL-NeurIPS24/src/algorithm/flfl.py 129da2b3defd51b4 unverified MIT (permissive)
This Probably Looks Exactly Like That: An Invertible Prototypical Network 16 Jul 2024 craymichael/ProtoFlow/protoflow/training.py c582f1abfdf87bbf ran GPL-2.0 (copyleft) · pointer only
Fast One-Stage Unsupervised Domain Adaptive Person Search 5 May 2024 whbdmu/FOUS/models/da_loss.py 8ed2bcdbe0cb49a9 ran · our draft was wrong no licence file found · pointer only
Dirichlet-Based Prediction Calibration for Learning with Noisy Labels 13 Jan 2024 chenchenzong/dpc/AAAI2024_DPC_code/Train_cifar_aug.py bb48f83128c0e944 unverified no licence file found · pointer only
Image Clustering with External Guidance 18 Oct 2023 xlearning-scu/2024-icml-tac/loss_utils.py fd5c16213c636e3f unverified no licence file found · pointer only
IOMatch: Simplifying Open-Set Semi-Supervised Learning with Joint Inliers and Outliers Utilization 25 Aug 2023 nukezil/IOMatch/semilearn/algorithms/utils/loss.py 9db92459be6a39f2 ran MIT (permissive)
MarginMatch: Improving Semi-Supervised Learning with Pseudo-Margins 17 Aug 2023 tsosea2/MarginMatch/models/marginmatch/marginmatch.py d3050212d4076500 ran · fixture could not drive it MIT (permissive)
SoftMatch: Addressing the Quantity-Quality Trade-off in Semi-supervised Learning 26 Jan 2023 torchssl/torchssl/models/softmatch/softmatch.py e4d292b8713ae955 ran · fixture could not drive it MIT (permissive)
Confidence-aware Training of Smoothed Classifiers for Certified Robustness 18 Dec 2022 alinlab/smoothing-catrs/code/consistency.py 3d45147f0a674bc0 unverified MIT (permissive)
Domain Adaptive Person Search 25 Jul 2022 caposerenity/DAPS/models/da_head.py 8ed2bcdbe0cb49a9 ran · our draft was wrong no licence file found · pointer only
FreeMatch: Self-adaptive Thresholding for Semi-supervised Learning 15 May 2022 torchssl/torchssl/models/freematch/freematch.py 8d4e503501f8115a ran · fixture could not drive it MIT (permissive)
BoostMIS: Boosting Medical Image Semi-supervised Learning with Adaptive Pseudo Labeling and Informative Active Annotation 4 Mar 2022 wannature/BoostMIS/models/boostmis/boostmis.py c5961573472dc412 ran no licence file found · pointer only
FlexMatch: Boosting Semi-Supervised Learning with Curriculum Pseudo Labeling 15 Oct 2021 torchssl/torchssl/models/flexmatch/flexmatch.py 8f89311db3715e3c ran · fixture could not drive it MIT (permissive)
FlexMatch: Boosting Semi-Supervised Learning with Curriculum Pseudo Labeling 15 Oct 2021 beandkay/sequencematch/models/flexmatch/flexmatch.py 45956241e5216b30 ran · fixture could not drive it MIT (permissive)
Consistency Regularization for Certified Robustness of Smoothed Classifiers 7 Jun 2020 jh-jeong/smoothing-consistency/code/consistency.py 3d45147f0a674bc0 unverified MIT (permissive)
FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence 21 Jan 2020 LeeDoYup/FixMatch-pytorch/models/fixmatch/fixmatch.py 4a5f8edc558790c2 ran · fixture could not drive it MIT (permissive)
FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence 21 Jan 2020 gomezzz/distmsmatch/MSMatch/models/fixmatch/FixMatch.py 5efbd3f44ce7dcf5 ran · fixture could not drive it MIT (permissive)
FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence 21 Jan 2020 gomezzz/MSMatch/models/fixmatch/fixmatch.py cc357ff67b149646 ran · fixture could not drive it MIT (permissive)
Domain Adaptive Faster R-CNN for Object Detection in the Wild 8 Mar 2018 krumo/Domain-Adaptive-Faster-RCNN-PyTorch/maskrcnn_benchmark/layers/consistency_loss.py 447d0d4c7a2722a4 unverified MIT recorded; this copy not marked cleared · pointer only

This site shows no code text; each File cell links to the file on GitHub at the repository's current default branch, which may have changed since the harvest. "Pointer only" means Syntology does not serve that copy's text, for one of four reasons: no licence file was found; the licence was not identified; the licence is recorded as permissive but that copy's record is not marked cleared; or the licence is outside the permissive list Syntology serves text under (MIT, Apache-2.0, BSD and similar). Some licences outside that list permit redistribution, such as WTFPL, and GPL-3.0 under its conditions; they are simply not on the list. Hover a licence cell for the reason. Per-sample records for a paper are on its paper page under "Code Syntology ran".

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