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image_train

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

image_train appears in the code Syntology harvested for 18 papers, as 10 distinct code bodies found in 19 places (a place is one code body under one paper). At least one of them ran in 13 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 image_train 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 6 of the 10 distinct code bodies named image_train; 4 are 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
0ran · fixture could not drive it
4ran
4unverified
0fingerprinted

Licence is a property of each copy, so it is counted per place: 6 of the 19 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

18 papers shown of 18, newest first; 19 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; 2 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
Rethinking Guidance Information to Utilize Unlabeled Samples:A Label Encoding Perspective 5 Jun 2024 zhangyl660/LERM/UDA/DA/CDAN-LERM/pre_process.py 782c9941712c32ae ran no licence file found · pointer only
DGMamba: Domain Generalization via Generalized State Space Model 11 Apr 2024 longshaocong/dgmamba/datautil/imgdata/util.py a58ba2e71a236bcb ran no licence file found · pointer only
On $f$-Divergence Principled Domain Adaptation: An Improved Framework 2 Feb 2024 thuml/CDAN/pytorch/pre_process.py 7ca82968fbea82c4 ran no licence file found · pointer only
Benchmarking Test-Time Adaptation against Distribution Shifts in Image Classification 6 Jul 2023 yuyongcan/benchmark-tta/train_source.py 35c2d572ebd185f5 ran · our draft was wrong no licence file found · pointer only
Divide and Contrast: Source-free Domain Adaptation via Adaptive Contrastive Learning 12 Nov 2022 zyezhang/dac/VisDA/target.py cd55b45f072d883a unverified GPL-3.0 (copyleft) · pointer only
Concurrent Subsidiary Supervision for Unsupervised Source-Free Domain Adaptation 27 Jul 2022 albert0147/sfda_neighbors/office-home/utils.py 620153a1ea154802 unverified MIT (permissive)
Prior Knowledge Guided Unsupervised Domain Adaptation 18 Jul 2022 tsun/KUDA/DINE/DINE_dist.py 35c2d572ebd185f5 ran · our draft was wrong MIT (permissive)
Confidence Score for Source-Free Unsupervised Domain Adaptation 14 Jun 2022 jhyun17/cowa-jmds/image_target_CoWA.py d9601c3485e6033d ran MIT (permissive)
Attracting and Dispersing: A Simple Approach for Source-free Domain Adaptation 9 May 2022 Albert0147/AaD_SFDA/tar_adaptation.py 35c2d572ebd185f5 ran · our draft was wrong no licence file found · pointer only
On Balancing Bias and Variance in Unsupervised Multi-Source-Free Domain Adaptation 1 Feb 2022 maohaos2/MSFDA/train_source.py 35c2d572ebd185f5 ran · our draft was wrong MIT (permissive)
On Balancing Bias and Variance in Unsupervised Multi-Source-Free Domain Adaptation 1 Feb 2022 maohaos2/MSFDA/adapt.py 9c0d2ceeef4fc6b3 unverified MIT (permissive)
Improving Mini-batch Optimal Transport via Partial Transportation 22 Aug 2021 khainb/BoMb-OT/PartialDA/run_mOT.py 6d14e3fcb8d93fa1 ran · our draft was wrong MIT (permissive)
Nearest Neighborhood-Based Deep Clustering for Source Data-absent Unsupervised Domain Adaptation 27 Jul 2021 tntek/N2DCX/object/N2DCEX_target.py 35c2d572ebd185f5 ran · our draft was wrong MIT (permissive)
DINE: Domain Adaptation from Single and Multiple Black-box Predictors 4 Apr 2021 tim-learn/Dis-tune/DINE_dist.py 35c2d572ebd185f5 ran · our draft was wrong MIT (permissive)
A Balanced and Uncertainty-aware Approach for Partial Domain Adaptation 5 Mar 2020 tim-learn/BA3US/run_partial.py 6d14e3fcb8d93fa1 ran · our draft was wrong MIT (permissive)
Central Similarity Quantization for Efficient Image and Video Retrieval 1 Aug 2019 yuanli2333/Hadamard-Matrix-for-hashing/pre_process.py 3331c5176304df6a unverified MIT (permissive)
HashNet: Deep Learning to Hash by Continuation 2 Feb 2017 thuml/HashNet/pytorch/src/pre_process.py 3331c5176304df6a unverified MIT (permissive)
arXiv:aaai_28150 lhrrrrrr/DiDA/utils/utils.py 620153a1ea154802 unverified Apache-2.0 (permissive)
arXiv:Liang_DINE_Domain_Adaptation_From_Single_and_Multiple_Black-Box_Predictors_CVPR_2022_paper tim-learn/DINE/DINE_dist.py 35c2d572ebd185f5 ran · our draft was wrong 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".

Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections