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mixup_criterion

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

mixup_criterion appears in the code Syntology harvested for 28 papers, as 13 distinct code bodies found in 28 places (a place is one code body under one paper). At least one of them ran in 24 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 mixup_criterion 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 10 of the 13 distinct code bodies named mixup_criterion; 3 are unverified. One tile per status, in the site's fixed vocabulary, each code body counted once:

0ran · honoured contract
0ran · violated contract
7ran · our draft was wrong
1ran · fixture could not drive it
2ran
3unverified
0fingerprinted

Licence is a property of each copy, so it is counted per place: 15 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

28 papers shown of 28, 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, and the graph's for 1 papers added by Syntology; 3 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
Scaling Up Temporal Domain Generalization via Temporal Experts Averaging added by Syntology 2025-09 (from id) zxcvfd13502/TEA/methods/mixup.py 97f484048ac03556 ran · our draft was wrong no licence file found · pointer only
Comprehensive Attribution: Inherently Explainable Vision Model with Feature Detector 27 Jul 2024 zood123/comet/train_module.py 97f484048ac03556 ran · our draft was wrong no licence file found · pointer only
Distribution-Aware Data Expansion with Diffusion Models 11 Mar 2024 haoweiz23/distdiff/augmentations/mixup.py 97f484048ac03556 ran · our draft was wrong no licence file found · pointer only
CLIF: Complementary Leaky Integrate-and-Fire Neuron for Spiking Neural Networks 7 Feb 2024 huuyulong/complementary-lif/utils/data_loaders.py 97f484048ac03556 ran · our draft was wrong no licence file found · pointer only
MIMONets: Multiple-Input-Multiple-Output Neural Networks Exploiting Computation in Superposition 5 Dec 2023 IBM/multiple-input-multiple-output-nets/MIMOConv/src/mixup.py 9b48dc4a035b2412 ran Apache-2.0 (permissive)
CED: Consistent ensemble distillation for audio tagging 2023-08 (from id) richermans/ced/utils.py f79c5ee46dcd36be ran GPL-3.0 (copyleft) · pointer only
Feature Fusion from Head to Tail for Long-Tailed Visual Recognition 12 Jun 2023 keke921/h2t/methods.py aed47bc4cea90b34 ran · our draft was wrong MIT (permissive)
Long-Tailed Class Incremental Learning 1 Oct 2022 xialeiliu/Long-Tailed-CIL/src/approach/LAS_utils.py 0016935c9f13abf7 unverified MIT (permissive)
SparCL: Sparse Continual Learning on the Edge 20 Sep 2022 identical code first harvested elsewhere 12031ff7668a8502 ran · our draft was wrong licence of this copy not recorded
UMIX: Improving Importance Weighting for Subpopulation Shift via Uncertainty-Aware Mixup 19 Sep 2022 tencentailabhealthcare/umix/examples/algorithms/UMIX.py 8e5d10cb937575b9 ran · our draft was wrong no licence file found · pointer only
Identifying Hard Noise in Long-Tailed Sample Distribution 27 Jul 2022 yxymessi/H2E-Framework/eccv_github/noise_longtail/code/utils.py 79edc1957c1e3747 unverified MIT (permissive)
One-Pixel Shortcut: on the Learning Preference of Deep Neural Networks 24 May 2022 cychomatica/one-pixel-shotcut/augmentation/Mixup.py 97f484048ac03556 ran · our draft was wrong Apache-2.0 (permissive)
From Modern CNNs to Vision Transformers: Assessing the Performance, Robustness, and Classification Strategies of Deep Learning Models in Histopathology 11 Apr 2022 hhi-aml/histobenchmark/code/main_patho_lightning.py 97f484048ac03556 ran · our draft was wrong no licence file found · pointer only
FedCorr: Multi-Stage Federated Learning for Label Noise Correction 10 Apr 2022 identical code first harvested elsewhere 97f484048ac03556 ran · our draft was wrong licence of this copy not recorded
SparseFed: Mitigating Model Poisoning Attacks in Federated Learning with Sparsification 12 Dec 2021 sparsefed/sparsefed/CommEfficient/cv_train.py 62225dd8920c913d ran · fixture could not drive it no licence file found · pointer only
MEST: Accurate and Fast Memory-Economic Sparse Training Framework on the Edge 26 Oct 2021 boone891214/MEST/main_sparse_train.py 12031ff7668a8502 ran · our draft was wrong no licence file found · pointer only
Improving Calibration for Long-Tailed Recognition 1 Apr 2021 Jia-Research-Lab/MiSLAS/methods.py 97f484048ac03556 ran · our draft was wrong MIT (permissive)
ResLT: Residual Learning for Long-tailed Recognition 26 Jan 2021 jiequancui/ResLT/CIFAR/cifarTrain_reslt_cifar10.py 9b2fdcdcc7de945a ran · our draft was wrong MIT (permissive)
Data Augmentation for Meta-Learning 14 Oct 2020 renkunni/metaaug/train_aug.py 5825603a0ae4a323 ran · our draft was wrong MIT (permissive)
High-Capacity Expert Binary Networks 7 Oct 2020 1adrianb/expert-binary-networks/utils/mixup.py 97f484048ac03556 ran · our draft was wrong MIT (permissive)
Class-Imbalanced Semi-Supervised Learning 17 Feb 2020 MinsungHyun/Class-Imbalanced-Semi-Supervised-Learning/CISSL_cls/lib/utils.py 79edc1957c1e3747 unverified MIT (permissive)
Gated Convolutional Networks with Hybrid Connectivity for Image Classification 26 Aug 2019 identical code first harvested elsewhere 33ba52fc17e89516 ran · our draft was wrong licence of this copy not recorded
Interpolated Adversarial Training: Achieving Robust Neural Networks without Sacrificing Too Much Accuracy 16 Jun 2019 identical code first harvested elsewhere 33ba52fc17e89516 ran · our draft was wrong licence of this copy not recorded
Adversarial Examples Are Not Bugs, They Are Features 6 May 2019 ndb796/pytorch-adversarial-training-cifar/interpolated_adversarial_training.py 33ba52fc17e89516 ran · our draft was wrong no licence file found · pointer only
Towards Deep Learning Models Resistant to Adversarial Attacks 19 Jun 2017 identical code first harvested elsewhere 33ba52fc17e89516 ran · our draft was wrong licence of this copy not recorded
arXiv:aaai_29262 Keke921/H2T/methods.py aed47bc4cea90b34 ran · our draft was wrong MIT (permissive)
arXiv:2021.emnlp-main.154 Hazelsuko07/TextHide/instahide.py 6588875b2a3f66d1 unverified MIT (permissive)
arXiv:136850478 leo-gb/UMA/ccs_training/models/mixup.py 97f484048ac03556 ran · our draft was wrong 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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