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get_num_classes

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

get_num_classes appears in the code Syntology harvested for 26 papers, as 19 distinct code bodies found in 26 places (a place is one code body under one paper). At least one of them ran in 13 of the papers; 2 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 get_num_classes 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 11 of the 19 distinct code bodies named get_num_classes; 8 are unverified. One tile per status, in the site's fixed vocabulary, each code body counted once:

6ran · honoured contract
0ran · violated contract
0ran · our draft was wrong
0ran · fixture could not drive it
5ran
8unverified
2fingerprinted

Licence is a property of each copy, so it is counted per place: 10 of the 26 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

26 papers shown of 26, newest first; 26 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 3 papers added by Syntology; 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 the Teacher-Student Framework for Test-Time Adaptation added by Syntology 2026-09 (from id) dmn-sjk/intransigent_teacher/conf.py 00d85985335c918a unverified no licence file found · pointer only
TabSCM: A practical Framework for Generating Realistic Tabular Data added by Syntology 2026-04 (from id) jsve96/TabSCM/tabscm/model.py a1fffc6a33fa28c3 ran · honoured contract Apache-2.0 (permissive)
The Golden Subspace: Where Efficiency Meets Generalization in Continual Test-Time Adaptation added by Syntology 2026-03 (from id) AIGNLAI/GOLD/classification/conf.py 4d83f34a9766b4a9 unverified no licence file found · pointer only
Revisiting Ensembling in One-Shot Federated Learning 11 Nov 2024 sacs-epfl/fens/aggregations.py 2509af88160cc0c7 ran · honoured contract no licence file found · pointer only
Certified Robustness for Deep Equilibrium Models via Serialized Random Smoothing 1 Nov 2024 WeizhiGao/Serialized-Randomized-Smoothing/SRS/code/datasets.py 8c99213918181c27 unverified MIT (permissive)
C2A: Client-Customized Adaptation for Parameter-Efficient Federated Learning 1 Nov 2024 yeachan-kr/c2a/utils.py 20a6e7ef558941b5 unverified no licence file found · pointer only
Distribution Alignment for Fully Test-Time Adaptation with Dynamic Online Data Streams 16 Jul 2024 WZq975/DA-TTA/conf.py ad7ee34b3774e46a ran MIT (permissive)
Effects of Exponential Gaussian Distribution on (Double Sampling) Randomized Smoothing 4 Jun 2024 tdano1/eg-on-smoothing/datasets.py d11c42096411b81b ran fingerprinted no licence file found · pointer only
OpenDlign: Open-World Point Cloud Understanding with Depth-Aligned Images 25 Apr 2024 Yebulabula/OpenDlign/zero_shot.py b2947808596ad0ac ran · honoured contract no licence file found · pointer only
Class-incremental Learning for Time Series: Benchmark and Evaluation 19 Feb 2024 zqiao11/tscil/utils/setup_elements.py 7fd412bc6308eb53 ran · honoured contract Apache-2.0 (permissive)
Un-Mixing Test-Time Normalization Statistics: Combatting Label Temporal Correlation 16 Jan 2024 devavratTomar/unmixtns/tta_experiments/conf.py a1872378501c3ba8 ran no licence file found · pointer only
Unraveling Batch Normalization for Realistic Test-Time Adaptation 15 Dec 2023 kiwi12138/realistictta/conf.py ad7ee34b3774e46a ran MIT (permissive)
In Search of Lost Online Test-time Adaptation: A Survey 31 Oct 2023 jo-wang/otta_vit_survey/classification/conf.py 772f46396ecd553a ran MIT (permissive)
Provably Cost-Sensitive Adversarial Defense via Randomized Smoothing 12 Oct 2023 trustmlrg/cs-rs/datasets.py 99b111869c0323bf ran fingerprinted no licence file found · pointer only
DiffSmooth: Certifiably Robust Learning via Diffusion Models and Local Smoothing 28 Aug 2023 javyduck/diffsmooth/datasets.py 8c99213918181c27 unverified no licence file found · pointer only
PREFER: Prompt Ensemble Learning via Feedback-Reflect-Refine 23 Aug 2023 zcrwind/prefer/prefer.py 01f18670482745e3 ran · honoured contract MIT (permissive)
Confidence-aware Training of Smoothed Classifiers for Certified Robustness 18 Dec 2022 alinlab/smoothing-catrs/code/datasets.py ddca6b8fb149d7f9 unverified MIT (permissive)
(Certified!!) Adversarial Robustness for Free! 21 Jun 2022 blaisedelattre/bridging_the_gap_rs/code/datasets.py 8c99213918181c27 unverified MIT (permissive)
GradSign: Model Performance Inference with Theoretical Insights 16 Oct 2021 cmu-catalyst/gradsign/zero-cost-nas-code/build.py dbc9b4d67b87c439 ran · honoured contract no licence file found · pointer only
An Orthogonal Classifier for Improving the Adversarial Robustness of Neural Networks 19 May 2021 MTandHJ/roboc/src/loadopts.py a0fcd7383413cd2a unverified MIT (permissive)
Home Action Genome: Cooperative Compositional Action Understanding 11 May 2021 nishantrai18/homage/train/model_utils.py d44d97ff27bc2448 unverified MIT (permissive)
Consistency Regularization for Certified Robustness of Smoothed Classifiers 7 Jun 2020 jh-jeong/smoothing-consistency/code/datasets.py 626fd2d46f9e5106 unverified MIT (permissive)
Random Smoothing Might be Unable to Certify $\ell_\infty$ Robustness for High-Dimensional Images 10 Feb 2020 hongyanz/TRADES-smoothing/code/datasets.py 8c99213918181c27 unverified MIT (permissive)
Provably Robust Deep Learning via Adversarially Trained Smoothed Classifiers 9 Jun 2019 azshue/boosting_robust/code/datasets.py 8c99213918181c27 unverified MIT (permissive)
arXiv:aaai_29436 kiwi12138/RealisticTTA/conf.py ad7ee34b3774e46a ran MIT (permissive)
arXiv:Ni_Maintaining_Consistent_Inter-Class_Topology_in_Continual_Test-Time_Adaptation_CVPR_2025_paper Successybbdwm/TCA/classification/conf.py 4d83f34a9766b4a9 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".

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