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gradient_penalty

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

gradient_penalty appears in the code Syntology harvested for 23 papers, as 25 distinct code bodies found in 28 places (a place is one code body under one paper). At least one of them ran in 12 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 gradient_penalty 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 25 distinct code bodies named gradient_penalty; 14 are unverified. One tile per status, in the site's fixed vocabulary, each code body counted once:

1ran · honoured contract
0ran · violated contract
6ran · our draft was wrong
3ran · fixture could not drive it
1ran
14unverified
2fingerprinted

Licence is a property of each copy, so it is counted per place: 8 of the 28 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

23 papers shown of 23, newest first; 28 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
RLVR-World: Training World Models with Reinforcement Learning 20 May 2025 thuml/RLVR-World/vid_wm/ivideogpt/train_ctx_tokenizer.py 1825d9e3299f556f unverified MIT (permissive)
Synthetic Tabular Data Generation for Class Imbalance and Fairness: A Comparative Study 8 Sep 2024 panagiotou/fairaugment/TabFairGAN_main/TabFairGAN_original.py 891e1a30409c7fec ran fingerprinted no licence file found · pointer only
Neural Collage Transfer: Artistic Reconstruction via Material Manipulation 3 Nov 2023 northadventure/CollageRL/env/collage.py a1ebae71cf1f6c5e ran · our draft was wrong licence not identified · pointer only
SDF-StyleGAN: Implicit SDF-Based StyleGAN for 3D Shape Generation 24 Jun 2022 zhengxinyang/sdf-stylegan/network/loss.py 2a12962bbf9f5fe6 unverified MIT (permissive)
Increasing the accuracy and resolution of precipitation forecasts using deep generative models 23 Mar 2022 raspstephan/nwp-downscale/src/trainer.py 25206bb7bf6ae8d9 ran · honoured contract fingerprinted MIT (permissive)
GANSeg: Learning to Segment by Unsupervised Hierarchical Image Generation 2 Dec 2021 xingzhehe/ganseg/train_gan.py 8b990fc5a9f8e51b ran · our draft was wrong no licence file found · pointer only
From EMBER to FIRE: predicting high resolution baryon fields from dark matter simulations with Deep Learning 2021-10 (from id) maurbe/ember/lib/utils.py 9591ee61de5669d4 unverified MIT (permissive)
TabFairGAN: Fair Tabular Data Generation with Generative Adversarial Networks 2 Sep 2021 amirarsalan90/TabFairGAN/src/tabfairgan/modules.py 891e1a30409c7fec ran fingerprinted MIT (permissive)
A Closer Look at Fourier Spectrum Discrepancies for CNN-generated Images Detection 31 Mar 2021 sutd-visual-computing-group/Fourier-Discrepancies-CNN-Detection/src/gans/gp.py e9717dca89fe2a5d unverified MIT (permissive)
Knowledge-Enriched Distributional Model Inversion Attacks 8 Oct 2020 SCccc21/Knowledge-Enriched-Distributional-Model-Inversion-Attacks/binary_gan.py b4bca36ffb2c3329 ran · our draft was wrong MIT (permissive)
Model Patching: Closing the Subgroup Performance Gap with Data Augmentation 15 Aug 2020 HazyResearch/model-patching/augmentation/methods/cyclegan/utils.py 1eb22022906e8429 unverified Apache-2.0 (permissive)
Anomaly Detection in Medical Imaging with Deep Perceptual Autoencoders 23 Jun 2020 ninatu/anomaly_detection/anomaly_detection/dpa/losses.py 404a50d962719722 unverified Apache-2.0 (permissive)
Conditional Sampling with Monotone GANs: from Generative Models to Likelihood-Free Inference 11 Jun 2020 baptistar/mgan/DarcyFlow/learn_mgan.py 25206bb7bf6ae8d9 ran · honoured contract fingerprinted MIT (permissive)
Deep Active Learning: Unified and Principled Method for Query and Training 20 Nov 2019 cjshui/WAAL/query_strategies/wasserstein_adversarial.py 1de7d5695f462332 ran · fixture could not drive it no licence file found · pointer only
The Secret Revealer: Generative Model-Inversion Attacks Against Deep Neural Networks 17 Nov 2019 AI-secure/GMI-Attack/Celeba/train_gan.py b4bca36ffb2c3329 ran · our draft was wrong no licence file found · pointer only
Hierarchical Mixtures of Generators for Adversarial Learning 5 Nov 2019 alper111/hmog/utils.py e7d2b0ddec14ac29 unverified MIT (permissive)
Missing Data Imputation with Adversarially-trained Graph Convolutional Networks 6 May 2019 spindro/GINN/ginn/models.py 810a18794c0c3062 ran · our draft was wrong Apache-2.0 (permissive)
A Style-Based Generator Architecture for Generative Adversarial Networks 12 Dec 2018 manicman1999/StyleGAN-Tensorflow-2.0/stylegan.py 59a58251d951132c unverified MIT (permissive)
Image Inpainting via Generative Multi-column Convolutional Neural Networks 20 Oct 2018 shepnerd/inpainting_gmcnn/pytorch/model/loss.py 685b99c187056ddf unverified MIT (permissive)
DeSIGN: Design Inspiration from Generative Networks 3 Apr 2018 blufzzz/Design-Inspiration-from-Generative-Networks/loss.py 7151d7e249d02031 unverified MIT (permissive)
XGAN: Unsupervised Image-to-Image Translation for Many-to-Many Mappings 14 Nov 2017 Gintasp/xgan/models/cdann.py 65b0abd517636e51 unverified Apache-2.0 (permissive)
Wasserstein Distance Guided Representation Learning for Domain Adaptation 5 Jul 2017 jvanvugt/pytorch-domain-adaptation/wdgrl.py 0fac189a2adbfa64 ran · our draft was wrong MIT (permissive)
Improved Training of Wasserstein GANs 31 Mar 2017 bigmao8576/WGAN-GP-Tensorflow2/WGAN_GP_mask.py 86a6bb90b9961d27 ran · fixture could not drive it no licence file found · pointer only
Improved Training of Wasserstein GANs 31 Mar 2017 spandan2/Wgan-GP_cats/wgan-gp.py e7dcf2e7ee3db27c ran · our draft was wrong no licence file found · pointer only
Improved Training of Wasserstein GANs 31 Mar 2017 LynnHo/DCGAN-LSGAN-WGAN-WGAN-GP-Tensorflow/tf2gan/loss.py db554be6d2613650 ran · fixture could not drive it MIT (permissive)
Improved Training of Wasserstein GANs 31 Mar 2017 raylyh/misgan-reimplementation/src/conv_misgan.py ca4a31233df66289 unverified no licence file found · pointer only
Improved Training of Wasserstein GANs 31 Mar 2017 marload/GANs-TensorFlow2/WGAN-GP/WGAN-GP.py a931b7024fe9731c unverified Apache-2.0 (permissive)
Improved Training of Wasserstein GANs 31 Mar 2017 ydataai/ydata-synthetic/src/data_synthetic/synthesizers/loss.py 317df542c2389065 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