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pgd

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

pgd appears in the code Syntology harvested for 18 papers, as 20 distinct code bodies found in 20 places (a place is one code body under one paper). At least one of them ran in 7 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 pgd 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 20 distinct code bodies named pgd; 13 are unverified. One tile per status, in the site's fixed vocabulary, each code body counted once:

0ran · honoured contract
0ran · violated contract
4ran · our draft was wrong
0ran · fixture could not drive it
3ran
13unverified
0fingerprinted

Licence is a property of each copy, so it is counted per place: 12 of the 20 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; 20 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; 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
arXiv:2507.12843 2025-07 (from id) zhijianzhouml/NAMMD/ADV_exp/attack_generator.py 28b14896c4e904e7 unverified no licence file found · pointer only
Robustness in Both Domains: CLIP Needs a Robust Text Encoder 3 Jun 2025 LIONS-EPFL/LEAF/src/robust_vlm/train/adversarial_training_clip.py f9a6cd97c45d4f91 ran · our draft was wrong no licence file found · pointer only
Breaking the Barrier: Enhanced Utility and Robustness in Smoothed DRL Agents 26 Jun 2024 trustworthy-ml-lab/robust_highutil_smoothed_drl/SDQN/attacks.py 5a2c00df265799e5 ran · our draft was wrong no licence file found · pointer only
Practical Region-level Attack against Segment Anything Models 12 Apr 2024 ShenYifanS/S-RA_T-RA/SSAscripts/example.py 83995d3bc2efe6df unverified no licence file found · pointer only
Practical Region-level Attack against Segment Anything Models 12 Apr 2024 ShenYifanS/S-RA_T-RA/SSAscripts/example_multi.py 93ebcf9c9ccea222 unverified no licence file found · pointer only
Regularized Adaptive Momentum Dual Averaging with an Efficient Inexact Subproblem Solver for Training Structured Neural Network 21 Mar 2024 ismoptgroup/ramda/Core/optimizer.py a445e118c7ac9c43 ran no licence file found · pointer only
Belief-Enriched Pessimistic Q-Learning against Adversarial State Perturbations 6 Mar 2024 SliencerX/Belief-enriched-robust-Q-learning/attacks.py b04a1a19e73c0ebb ran no licence file found · pointer only
Robust CLIP: Unsupervised Adversarial Fine-Tuning of Vision Embeddings for Robust Large Vision-Language Models 19 Feb 2024 chs20/robustvlm/train/adversarial_training_clip.py 7062c33b2cc96026 ran · our draft was wrong MIT (permissive)
DAFA: Distance-Aware Fair Adversarial Training 23 Jan 2024 rucy74/DAFA/evaluation.py b1ca971f9ac6b177 ran no licence file found · pointer only
Outlier Robust Adversarial Training 10 Sep 2023 discovershu/orat/attack_generator.py 2ea2c513cf37e3ab unverified no licence file found · pointer only
Outlier Robust Adversarial Training 10 Sep 2023 discovershu/orat/attack_generator_for_cifar100.py 0dd83d1a63902700 unverified no licence file found · pointer only
AROID: Improving Adversarial Robustness Through Online Instance-Wise Data Augmentation 12 Jun 2023 treelli/aroid/src/utils/adversary.py 2fc76dc36847c6db unverified MIT (permissive)
Improving Adversarial Robustness of DEQs with Explicit Regulations Along the Neural Dynamics 2 Jun 2023 minicheshire/deq-regulating-neural-dynamics/input-entropy-reduction/MDEQ-Vision/lib/core/cls_function.py 304e362a32930910 ran · our draft was wrong no licence file found · pointer only
Data Augmentation Alone Can Improve Adversarial Training 24 Jan 2023 treelli/da-alone-improves-at/src/utils/adversary.py e36d3bdff03face5 unverified MIT (permissive)
Boosting the Transferability of Adversarial Attacks with Reverse Adversarial Perturbation 12 Oct 2022 Alan-Qin/Transfer_attack_RAP/rap_attack.py 01c6e50ac9a6dc7e unverified no licence file found · pointer only
A2: Efficient Automated Attacker for Boosting Adversarial Training 7 Oct 2022 alipay/A2-efficient-automated-attacker-for-boosting-adversarial-training/attack_generator.py cdf5b1330544a607 unverified Apache-2.0 (permissive)
Learn2Perturb: an End-to-end Feature Perturbation Learning to Improve Adversarial Robustness 2 Mar 2020 Ahmadreza-Jeddi/Learn2Perturb/attacks/pgd.py e0919d109e7e3ae4 unverified MIT (permissive)
Adversarial Attack Generation Empowered by Min-Max Optimization 9 Jun 2019 wangjksjtu/minmax-adv/neurips21/attacks/pgd.py fed551f077ea431d unverified MIT (permissive)
Unlabeled Data Improves Adversarial Robustness 31 May 2019 yguooo/semisup-adv/attack_pgd.py a23942fc2c20b6ab unverified MIT (permissive)
Semantic Adversarial Attacks: Parametric Transformations That Fool Deep Classifiers 17 Apr 2019 ameya005/Semantic_Adversarial_Attacks/simple_classifier.py e2dc3218a6608cc0 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