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vgg11_bn

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

vgg11_bn appears in the code Syntology harvested for 40 papers, as 34 distinct code bodies found in 41 places (a place is one code body under one paper). At least one of them ran in 12 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 vgg11_bn 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 34 distinct code bodies named vgg11_bn; 27 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
0ran · fixture could not drive it
6ran
27unverified
0fingerprinted

Licence is a property of each copy, so it is counted per place: 10 of the 41 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

40 papers shown of 40, newest first; 41 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; 5 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
On the Role of Label Noise in the Feature Learning Process 25 May 2025 zzp1012/label-noise-theory/src/model/cifar_vgg.py 174e080f41432e65 unverified MIT (permissive)
Continuous Thought Machines 8 May 2025 huyvnphan/PyTorch_CIFAR10/cifar10_models/vgg.py e569c3a2806d4b22 unverified MIT (permissive)
DASH: Warm-Starting Neural Network Training in Stationary Settings without Loss of Plasticity 30 Oct 2024 baekrok/DASH-Direction-Aware-SHrinking/models/VGG.py e8ac6d6137c1707b unverified MIT (permissive)
SAMPa: Sharpness-aware Minimization Parallelized 14 Oct 2024 LIONS-EPFL/SAMPa/model/vgg.py 09c336c3ecb596a3 ran no licence file found · pointer only
Hybrid Mamba for Few-Shot Segmentation 29 Sep 2024 sam1224/hmnet/model/vgg.py bf96fb236a6b1683 ran licence not identified · pointer only
Benchmarking the Attribution Quality of Vision Models 16 Jul 2024 visinf/idsds/models/vgg.py df6cfaa1fbccd91a unverified Apache-2.0 (permissive)
Eliminating Feature Ambiguity for Few-Shot Segmentation 13 Jul 2024 sam1224/aenet/HDMNet/model/vgg.py bf96fb236a6b1683 ran no licence file found · pointer only
Do Deep Neural Network Solutions Form a Star Domain? 12 Mar 2024 aktsonthalia/starlight/models/vgg.py 2dbd65bc233a2ffe ran MIT (permissive)
VRP-SAM: SAM with Visual Reference Prompt 27 Feb 2024 syp2ysy/vrp-sam/model/base/vgg.py bf96fb236a6b1683 ran MIT (permissive)
Detection and Defense of Unlearnable Examples 14 Dec 2023 hala64/udp/model/VGG.py 2b1f218ccb1f2dd2 ran no licence file found · pointer only
Ternary Spike: Learning Ternary Spikes for Spiking Neural Networks 11 Dec 2023 yfguo91/ternary-spike/models/vggcifar.py ff89b5bc32b8bfd8 unverified no licence file found · pointer only
Deep Unlearning: Fast and Efficient Gradient-free Approach to Class Forgetting 1 Dec 2023 sangamesh-kodge/class_forgetting/models/vgg.py ea6c4b4731920685 unverified no licence file found · pointer only
ReWaRD: Retinal Waves for Pre-Training Artificial Neural Networks Mimicking Real Prenatal Development 28 Nov 2023 bennyca/reward/4_Visualization/nets/vgg.py 1a5af0592a5260dc unverified licence not identified · pointer only
Stable Unlearnable Example: Enhancing the Robustness of Unlearnable Examples via Stable Error-Minimizing Noise 22 Nov 2023 liuyixin-louis/stable-unlearnable-example/models/vgg.py acc8c58f222a7df0 ran licence not identified · pointer only
Breaking the Trilemma of Privacy, Utility, Efficiency via Controllable Machine Unlearning 28 Oct 2023 guangyaodou/conmu/cv_models/VGG.py 2617bb063e662998 ran MIT (permissive)
FunnyBirds: A Synthetic Vision Dataset for a Part-Based Analysis of Explainable AI Methods 11 Aug 2023 visinf/funnybirds/funnybirds_complete/models/vgg.py df6cfaa1fbccd91a unverified Apache-2.0 (permissive)
Why Does Little Robustness Help? A Further Step Towards Understanding Adversarial Transferability 15 Jul 2023 cgcl-codes/transferattacksurrogates/TransferAttack/models/vgg.py acc8c58f222a7df0 ran MIT (permissive)
Learning-to-Rank Meets Language: Boosting Language-Driven Ordering Alignment for Ordinal Classification 24 Jun 2023 xk-huang/OrdinalCLIP/ordinalclip/models/image_encoders/vgg.py 71569392426397ab unverified MIT (permissive)
SplineCam: Exact Visualization and Characterization of Deep Network Geometry and Decision Boundaries 24 Feb 2023 ahmedimtiazprio/splinecam/splinecam/models.py d13139f6d7bfd8d6 unverified MIT (permissive)
Shift-tolerant Perceptual Similarity Metric 27 Jul 2022 abhijay9/shifttolerant-lpips/models_lpf/vgg.py 37a0395593656d08 unverified BSD-2-Clause (permissive)
An Empirical Study of Personalized Federated Learning 27 Jun 2022 onizukalab/fedbench/code/utils/model.py f7cd4a2b57319df7 unverified MIT (permissive)
Topology-aware Generalization of Decentralized SGD 25 Jun 2022 raiden-zhu/generalization-of-dsgd/gpu_work.py 617b89aca9a6c336 ran · our draft was wrong no licence file found · pointer only
The Manifold Hypothesis for Gradient-Based Explanations 15 Jun 2022 tml-tuebingen/explanations-manifold/replicate-paper/other_datasets/vgg.py e569c3a2806d4b22 unverified MIT (permissive)
On the Eigenvalues of Global Covariance Pooling for Fine-grained Visual Recognition 26 May 2022 KingJamesSong/DifferentiableSVD/src/network/vgg.py 57ac34428a02311d unverified Apache-2.0 (permissive)
Learning What Not to Segment: A New Perspective on Few-Shot Segmentation 15 Mar 2022 chunbolang/BAM/model/vgg.py bf96fb236a6b1683 ran MIT (permissive)
ProgFed: Effective, Communication, and Computation Efficient Federated Learning by Progressive Training 11 Oct 2021 hui-po-wang/progfed/models/vgg.py 305019eddb7a5f28 unverified MIT (permissive)
Phase Collapse in Neural Networks 11 Oct 2021 florentinguth/phasecollapse/models/vgg.py 9424fcdf454449da unverified BSD-3-Clause (permissive)
Few-Shot Segmentation with Global and Local Contrastive Learning 11 Aug 2021 liuweide01/GQNet-Few-shot-segmentation/model/vgg.py bf96fb236a6b1683 ran MIT (permissive)
Polygonal Unadjusted Langevin Algorithms: Creating stable and efficient adaptive algorithms for neural networks 28 May 2021 DongyoungLim/THEO_POULA/models/vgg.py b98435ae05182b25 unverified MIT (permissive)
Learning Black-Box Attackers with Transferable Priors and Query Feedback 21 Oct 2020 TrustworthyDL/LeBA/imagenet/models/vgg.py 4bafe38c1d715c07 unverified Apache-2.0 (permissive)
AdvFlow: Inconspicuous Black-box Adversarial Attacks using Normalizing Flows 15 Jul 2020 hmdolatabadi/AdvFlow/vgg.py dbe86db498d00951 unverified MIT (permissive)
Blind Backdoors in Deep Learning Models 8 May 2020 ebagdasa/backdoors101/models/vgg.py 5c9b2e9b83552cf6 unverified MIT (permissive)
Iterative Normalization: Beyond Standardization towards Efficient Whitening 6 Apr 2019 huangleiBuaa/IterNorm-pytorch/ImageNet/models/vgg.py 16c88c00e897cd0a unverified BSD-2-Clause (permissive)
Variational Adversarial Active Learning 31 Mar 2019 sinhasam/vaal/vgg.py fb01d604c8bf7aa2 unverified BSD-2-Clause (permissive)
Variational Adversarial Active Learning 31 Mar 2019 johntiger1/vaal_querying/vgg.py 2a49eb37cf4d8847 unverified BSD-2-Clause (permissive)
Many Task Learning with Task Routing 28 Mar 2019 gstrezoski/TaskRouting/routed_vgg.py 1cce7b73b2c6e63c unverified no licence file found · pointer only
arXiv:aaai_33972 sangamesh-kodge/SAP/models/vgg.py 5a9e5f8cb1d2d696 unverified Apache-2.0 (permissive)
arXiv:aaai_28258 dongmo-qcq/FG-CAM/models/vgg.py 7512164ac05c0917 unverified MIT (permissive)
arXiv:Pang_Backdoor_Cleansing_With_Unlabeled_Data_CVPR_2023_paper luluppang/BCU/models/vgg.py 4dbe602a3c7fef59 unverified MIT (permissive)
arXiv:136780474 dengyecode/hourglassattention/model/vgg.py 16bb9df35979ec55 unverified MIT (permissive)
arXiv:03594 zouyuda220/SAE/models/vgg_.py dc0345cf5f5d20b4 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