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load_cifar10

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

load_cifar10 appears in the code Syntology harvested for 20 papers, as 18 distinct code bodies found in 20 places (a place is one code body under one paper). At least one of them ran in 1 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 load_cifar10 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 1 of the 18 distinct code bodies named load_cifar10; 17 are unverified. One tile per status, in the site's fixed vocabulary, each code body counted once:

0ran · honoured contract
0ran · violated contract
0ran · our draft was wrong
0ran · fixture could not drive it
1ran
17unverified
0fingerprinted

Licence is a property of each copy, so it is counted per place: 5 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

20 papers shown of 20, 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, and the graph's for 2 papers added by Syntology. 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
Robustness Cannot be Reduced to Regularization: Studying Adversarial Training Beyond the Linear Case added by Syntology 2026-06 (from id) RobustBench/robustbench/robustbench/data.py 5ea0ef1ed4fe208c unverified licence not identified · pointer only
K-Way Energy Probes for Metacognition Reduce to Softmax in Discriminative Predictive Coding Networks added by Syntology 2026-04 (from id) synthiumjp/ima/src/cifar10_data.py 94203b1eb6857141 unverified no licence file found · pointer only
Shedding More Light on Robust Classifiers under the lens of Energy-based Models 8 Jul 2024 omnai-lab/robust-classifiers-under-the-lens-of-ebm/image_generation/utils.py f4b0898be0b428b7 unverified MIT (permissive)
Dynamic Domains, Dynamic Solutions: DPCore for Continual Test-Time Adaptation 15 Jun 2024 zybeich/DPCore/imagenet/robustbench/data.py 2ef65c34f0fa8042 unverified MIT (permissive)
MeanSparse: Post-Training Robustness Enhancement Through Mean-Centered Feature Sparsification 9 Jun 2024 spin-umass/meansparse/CIFAR100_Linfinity/data.py 2ea97919e65e55d9 unverified no licence file found · pointer only
Decomposing the Neurons: Activation Sparsity via Mixture of Experts for Continual Test Time Adaptation 26 May 2024 royzry98/moase-pytorch/robustbench/data.py 2ef65c34f0fa8042 unverified no licence file found · pointer only
Spanning Training Progress: Temporal Dual-Depth Scoring (TDDS) for Enhanced Dataset Pruning 22 Nov 2023 zhangxin-xd/Dataset-Pruning-TDDS/data.py daa31faafe9d2c13 unverified MIT (permissive)
Benchmarking Test-Time Adaptation against Distribution Shifts in Image Classification 6 Jul 2023 yuyongcan/benchmark-tta/robustbench/data.py 2ef65c34f0fa8042 unverified no licence file found · pointer only
FAM: Relative Flatness Aware Minimization 5 Jul 2023 kampmichael/RelativeFlatnessAndGeneralization/CorrelationFlatnessGeneralization/data_loaders.py 535c69c58c3d899b ran Apache-2.0 (permissive)
AutoBalance: Optimized Loss Functions for Imbalanced Data 4 Jan 2022 ucr-optml/autobalance/dataset/cifar10.py 8bf3be98e6cb32cb unverified MIT (permissive)
Photometric Redshifts from SDSS Images with an Interpretable Deep Capsule Network 2021-12 (from id) biprateep/encapZulate-1/src/encapzulate/data_loader/data_loader.py d918fcad643bf10e unverified MIT (permissive)
Enabling Fast Differentially Private SGD via Just-in-Time Compilation and Vectorization 18 Oct 2020 TheSalon/fast-dpsgd/data.py b61d063e75caff81 unverified MIT (permissive)
AMC-Loss: Angular Margin Contrastive Loss for Improved Explainability in Image Classification 21 Apr 2020 hchoi71/AMC-Loss/dataset.py 5a30aefe53a9d152 unverified MIT (permissive)
Logit Pairing Methods Can Fool Gradient-Based Attacks 29 Oct 2018 uds-lsv/evaluating-logit-pairing-methods/mnist_cifar10/utils.py 80ab7f0d5badcb2c unverified Apache-2.0 (permissive)
The relativistic discriminator: a key element missing from standard GAN 2 Jul 2018 taki0112/RelativisticGAN-Tensorflow/utils.py 66e93b6f5e7a8457 unverified MIT (permissive)
Self-Attention Generative Adversarial Networks 21 May 2018 taki0112/Self-Attention-GAN-Tensorflow/utils.py 51fd45e7747d0b80 unverified MIT (permissive)
Cost-Effective Active Learning for Deep Image Classification 13 Jan 2017 tueboesen/Active-Learning/src/dataloader.py 69536dd4fda5a080 unverified MIT (permissive)
How far can we go without convolution: Improving fully-connected networks 9 Nov 2015 AvocadoAlpha/boltz_bo/boltzmann_machines/utils/dataset.py 8458ac32d9281d6c unverified MIT (permissive)
Explaining and Harnessing Adversarial Examples 20 Dec 2014 BendeguzToth/Fun-with-ConvNets/project/utils.py 4bbe5492f940117b unverified MIT (permissive)
Very Deep Convolutional Networks for Large-Scale Image Recognition 4 Sep 2014 Vivswan/ECE-3195-Course-Project/src/load_cifar10.py 79aea1123cdd414d 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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