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classification_loss

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

classification_loss appears in the code Syntology harvested for 9 papers, as 10 distinct code bodies found in 10 places (a place is one code body under one paper). At least one of them ran in 2 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 classification_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 2 of the 10 distinct code bodies named classification_loss; 8 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
1ran · fixture could not drive it
0ran
8unverified
0fingerprinted

Licence is a property of each copy, so it is counted per place: 3 of the 10 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

9 papers shown of 9, newest first; 10 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. 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
C-SFDA: A Curriculum Learning Aided Self-Training Framework for Efficient Source Free Domain Adaptation 30 Mar 2023 nazmul-karim170/C-SFDA_Source-Free-Domain-Adaptation/target_csfda.py 8f6d0dd0f7edbb34 unverified MIT (permissive)
SIEDOB: Semantic Image Editing by Disentangling Object and Background 23 Mar 2023 WuyangLuo/SIEDOB/background/loss.py 1e8b9a8bd3d8e8e6 unverified MIT (permissive)
Contrastive Test-Time Adaptation 21 Apr 2022 DianCh/AdaContrast/target.py 4769519334699711 ran · our draft was wrong no licence file found · pointer only
Rethinking Depth Estimation for Multi-View Stereo: A Unified Representation 5 Jan 2022 prstrive/unimvsnet/loss.py d2cef21715760206 unverified MIT (permissive)
Explaining machine-learned particle-flow reconstruction 24 Nov 2021 faroukmokhtar/particleflow/mlpf/model/losses.py e64cf35ab8bff4bb unverified Apache-2.0 (permissive)
Representation Learning via Global Temporal Alignment and Cycle-Consistency 11 May 2021 hadjisma/VideoAlignment/d2tw/smoothDTW.py e3d8c79482d57679 ran · fixture could not drive it licence not identified · pointer only
RPM-Net: Robust Point Matching using Learned Features 30 Mar 2020 vinits5/masknet/learning3d/losses/classification.py 78cd571bf9c38cc0 unverified MIT recorded; this copy not marked cleared · pointer only
Temporal Cycle-Consistency Learning 16 Apr 2019 June01/tcc_Temporal_Cycle_Consistency_Loss.pytorch/tcc/losses.py 0a5209e8963a11c5 unverified Apache-2.0 (permissive)
Temporal Cycle-Consistency Learning 16 Apr 2019 June01/tcc_Temporal_Cycle_Consistency_Loss.pytorch/tcc_tf/losses.py 68a52026fa7e6e22 unverified Apache-2.0 (permissive)
Unsupervised Deep Embedding for Clustering Analysis 19 Nov 2015 Derek-Wds/MAD-VAE/utils/loss_function.py 7aa6390897fe4c2b unverified 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