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Brightness

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

Brightness appears in the code Syntology harvested for 31 papers, as 8 distinct code bodies found in 33 places (a place is one code body under one paper). At least one of them ran in 30 of the papers; 0 of the code bodies carry 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 Brightness 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 7 of the 8 distinct code bodies named Brightness; 1 is unverified. One tile per status, in the site's fixed vocabulary, each code body counted once:

0ran · honoured contract
0ran · violated contract
2ran · our draft was wrong
2ran · fixture could not drive it
3ran
1unverified
0fingerprinted

Licence is a property of each copy, so it is counted per place: 12 of the 33 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

31 papers shown of 31, newest first; 33 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 1 papers added by Syntology; 6 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
FedXDS: Leveraging Model Attribution Methods to counteract Data Heterogeneity in Federated Learning added by Syntology 2026-06 (from id) MaxH1996/FedXDS/augment.py 0d1a8d6ce2015fc7 ran no licence file found · pointer only
Watermark Anything with Localized Messages 11 Nov 2024 facebookresearch/watermark-anything/watermark_anything/models/wam.py 615d578f9886ba11 unverified MIT (permissive)
OwMatch: Conditional Self-Labeling with Consistency for Open-World Semi-Supervised Learning 4 Nov 2024 niusj03/OwMatch/randaugment.py 0d1a8d6ce2015fc7 ran MIT (permissive)
Continuous Contrastive Learning for Long-Tailed Semi-Supervised Recognition 8 Oct 2024 zhouzihao11/CCL/dataset/randaugment.py 0d1a8d6ce2015fc7 ran no licence file found · pointer only
Learning to Complement and to Defer to Multiple Users 9 Jul 2024 zhengzhang37/lecodu/utils/randaug.py 61ed09bbdb8adb72 ran no licence file found · pointer only
Boosting Consistency in Dual Training for Long-Tailed Semi-Supervised Learning 19 Jun 2024 gank0078/boat/dataset/randaugment.py 0d1a8d6ce2015fc7 ran no licence file found · pointer only
Erasing the Bias: Fine-Tuning Foundation Models for Semi-Supervised Learning 20 May 2024 Gank0078/FineSSL/datasets/randaugment.py 0d1a8d6ce2015fc7 ran no licence file found · pointer only
DreamDA: Generative Data Augmentation with Diffusion Models 19 Mar 2024 yunxiangfu2001/dreamda/dataset/augmentation.py 61ed09bbdb8adb72 ran no licence file found · pointer only
Robust Training of Federated Models with Extremely Label Deficiency 22 Feb 2024 tmlr-group/Twin-sight/randaugment.py 0d1a8d6ce2015fc7 ran MIT (permissive)
SimPro: A Simple Probabilistic Framework Towards Realistic Long-Tailed Semi-Supervised Learning 21 Feb 2024 leaplabthu/simpro/SimPro/dataset/randaugment.py 0d1a8d6ce2015fc7 ran no licence file found · pointer only
A General Framework for Learning from Weak Supervision 2 Feb 2024 hhhhhhao/general-framework-weak-supervision/src/datasets/rand_aug.py 61ed09bbdb8adb72 ran no licence file found · pointer only
Self-supervised Feature Adaptation for 3D Industrial Anomaly Detection 6 Jan 2024 yuanpengtu/LSFA/Adaptation/randaugment.py 0d1a8d6ce2015fc7 ran no licence file found · pointer only
Federated Learning with Extremely Noisy Clients via Negative Distillation 20 Dec 2023 linchen99/fedned/dataset/utils/randaugment.py 0d1a8d6ce2015fc7 ran no licence file found · pointer only
Transferable Candidate Proposal with Bounded Uncertainty 7 Dec 2023 gokyeongryeol/TBU/active/dataset/augment.py c83f67dd85072e2d ran MIT (permissive)
Robust Data Pruning under Label Noise via Maximizing Re-labeling Accuracy 2 Nov 2023 kaist-dmlab/Prune4Rel/randaugment.py 0d1a8d6ce2015fc7 ran MIT (permissive)
When to Learn What: Model-Adaptive Data Augmentation Curriculum 9 Sep 2023 JackHck/MADAug/adaptive_augmentor.py 75ed82aa32df43b7 ran · fixture could not drive it no licence file found · pointer only
Cluster-aware Semi-supervised Learning: Relational Knowledge Distillation Provably Learns Clustering 20 Jul 2023 dyjdongyijun/Semi_Supervised_Knowledge_Distillation/dataset/randaugment.py 0d1a8d6ce2015fc7 ran MIT (permissive)
CUDA: Curriculum of Data Augmentation for Long-Tailed Recognition 10 Feb 2023 sumyeongahn/cuda_ltr/cifar/aug/cuda.py b8c590bef6e963a5 ran · our draft was wrong no licence file found · pointer only
Not All Poisons are Created Equal: Robust Training against Data Poisoning 18 Oct 2022 yuyang0901/effective-poison-identification/dataset/randaugment.py 0d1a8d6ce2015fc7 ran MIT (permissive)
Fix-A-Step: Semi-supervised Learning from Uncurated Unlabeled Data 25 Aug 2022 tufts-ml/fix-a-step/src_CIFAR10/randaugment.py 0d1a8d6ce2015fc7 ran MIT (permissive)
OpenMatch: Open-set Consistency Regularization for Semi-supervised Learning with Outliers 28 May 2021 VisionLearningGroup/OP_Match/dataset/randaugment.py 0d1a8d6ce2015fc7 ran MIT (permissive)
Contrastive Learning with Stronger Augmentations 15 Apr 2021 maple-research-lab/CLSA/data_processing/RandAugment.py 0d1a8d6ce2015fc7 ran MIT (permissive)
Meta Pseudo Labels 23 Mar 2020 ifsheldon/MPL_Lightning/mpl_lightning/augmentation.py 0d1a8d6ce2015fc7 ran MIT (permissive)
FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence 21 Jan 2020 kekmodel/FixMatch-pytorch/dataset/randaugment.py f82e12f0d695e01b ran · our draft was wrong MIT (permissive)
FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence 21 Jan 2020 S-HuaBomb/FixMatch-Paddle/dataset/randaugment.py 0d1a8d6ce2015fc7 ran Apache-2.0 (permissive)
Fast AutoAugment 1 May 2019 tgilewicz/uniformaugment/UniformAugment/augmentations.py 993134d3c95bcf92 ran · fixture could not drive it MIT (permissive)
arXiv:aaai_29654 zcy866/CSR/augmentation.py 0d1a8d6ce2015fc7 ran MIT (permissive)
arXiv:aaai_29519 wu-dd/DIRK/augment/randaugment.py 61ed09bbdb8adb72 ran MIT (permissive)
arXiv:aaai_29068 NJUyued/SoC4SS-FGVC/lib/datasets/augmentation/randaugment.py 61ed09bbdb8adb72 ran MIT (permissive)
arXiv:aaai_28150 lhrrrrrr/DiDA/transforms/RandAugment.py 0d1a8d6ce2015fc7 ran Apache-2.0 (permissive)
arXiv:aaai_28150 lhrrrrrr/DiDA/utils/randaugment.py 61ed09bbdb8adb72 ran Apache-2.0 (permissive)
arXiv:Huang_Systematic_Comparison_of_Semi-supervised_and_Self-supervised_Learning_for_Medical_Image_CVPR_2024_paper tufts-ml/SSL-vs-SSL-benchmark/src/randaugment.py 0d1a8d6ce2015fc7 ran MIT (permissive)
arXiv:136970483 exped1230/S2-VER/datasets/augmentation/randaugment.py 61ed09bbdb8adb72 ran MIT (permissive)

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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