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preprocess_input

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

preprocess_input appears in the code Syntology harvested for 26 papers, as 33 distinct code bodies found in 43 places (a place is one code body under one paper). At least one of them ran in 4 of the papers; 1 of the code bodies carries 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 preprocess_input 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 4 of the 33 distinct code bodies named preprocess_input; 29 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
1ran · fixture could not drive it
2ran
29unverified
1fingerprinted

Licence is a property of each copy, so it is counted per place: 4 of the 43 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

26 papers shown of 26, newest first; 43 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
Less is More: Efficient Black-box Attribution via Minimal Interpretable Subset Selection 1 Apr 2025 RuoyuChen10/LIMA/baseline_attribution/debug_org_attribution_method_clip_rn101.py 9c033200afa5fa21 unverified MIT (permissive)
Less is More: Efficient Black-box Attribution via Minimal Interpretable Subset Selection 1 Apr 2025 RuoyuChen10/LIMA/baseline_attribution/debug_org_attribution_method_imagebind_vggsound.py 91a062e7f2b9ebcc unverified MIT (permissive)
Less is More: Efficient Black-box Attribution via Minimal Interpretable Subset Selection 1 Apr 2025 RuoyuChen10/LIMA/baseline_attribution/debug_org_attribution_method_mambavision.py 26868c1d763a8351 unverified MIT (permissive)
Less is More: Efficient Black-box Attribution via Minimal Interpretable Subset Selection 1 Apr 2025 RuoyuChen10/LIMA/baseline_attribution/debug_org_attribution_method_swin.py 68be2c3ac63cb2ac unverified MIT (permissive)
How Interpretable are Reasoning Explanations from Prompting Large Language Models? 19 Feb 2024 wj210/cot_interpretability/utils/utils.py 5ff002e9bf012277 ran no licence file found · pointer only
Towards Efficient Methods in Medical Question Answering using Knowledge Graph Embeddings 15 Jan 2024 saptarshi059/cdqa-project/run_modeling.py 9083b4c1b5c34754 unverified no licence file found · pointer only
NormDial: A Comparable Bilingual Synthetic Dialog Dataset for Modeling Social Norm Adherence and Violation 23 Oct 2023 Aochong-Li/NormDial/Normdial_code/dialogue_turn_labeling.py 3159890a3fd2b841 ran no licence file found · pointer only
Window-Based Early-Exit Cascades for Uncertainty Estimation: When Deep Ensembles are More Efficient than Single Models 14 Mar 2023 keras-team/keras/keras/src/applications/imagenet_utils.py fe1fee1f08d6f46d unverified Apache-2.0 (permissive)
Window-Based Early-Exit Cascades for Uncertainty Estimation: When Deep Ensembles are More Efficient than Single Models 14 Mar 2023 keras-team/keras/keras/src/applications/inception_v3.py 86361b3f206f4002 unverified Apache-2.0 (permissive)
Window-Based Early-Exit Cascades for Uncertainty Estimation: When Deep Ensembles are More Efficient than Single Models 14 Mar 2023 keras-team/keras/keras/src/applications/mobilenet.py b3bae248d68eaa66 unverified Apache-2.0 (permissive)
Window-Based Early-Exit Cascades for Uncertainty Estimation: When Deep Ensembles are More Efficient than Single Models 14 Mar 2023 keras-team/keras/keras/src/applications/mobilenet_v2.py dc94020311204486 unverified Apache-2.0 (permissive)
Harmonizing the object recognition strategies of deep neural networks with humans 8 Nov 2022 serre-lab/harmonization/harmonization/models/preprocess.py 41aa39036593bcbf unverified MIT (permissive)
Semantic Image Synthesis via Diffusion Models 30 Jun 2022 weilunwang/semantic-diffusion-model/image_sample.py 37c0096a424e9da1 ran · our draft was wrong no licence file found · pointer only
Global Context Vision Transformers 20 Jun 2022 shkarupa-alex/tfgcvit/tfgcvit/prep.py 9cac895861c9b777 unverified MIT (permissive)
Visual Attention Network 20 Feb 2022 shkarupa-alex/tfvan/tfvan/prep.py 5e6f2f4822f14d6f unverified MIT (permissive)
PraNet: Parallel Reverse Attention Network for Polyp Segmentation 13 Jun 2020 Thehunk1206/PRANet-Polyps-Segmentation/polyps_seg_video.py 77cacc9801b27be9 unverified MIT (permissive)
DAmageNet: A Universal Adversarial Dataset 16 Dec 2019 XingLiangLondon/Image-Similarity-in-Percentage/imagenet_utils.py 64eb4bea3d3127c4 unverified MIT (permissive)
DAmageNet: A Universal Adversarial Dataset 16 Dec 2019 XingLiangLondon/Image-Similarity-in-Percentage/inception_resnet_v2.py 17e355a0c16b9297 unverified MIT (permissive)
DAmageNet: A Universal Adversarial Dataset 16 Dec 2019 XingLiangLondon/Image-Similarity-in-Percentage/mobilenet.py bee766c6e15044c2 unverified MIT (permissive)
DAmageNet: A Universal Adversarial Dataset 16 Dec 2019 XingLiangLondon/Image-Similarity-in-Percentage/audio_conv_utils.py 73ad129b7b35ec98 unverified MIT (permissive)
When Does Label Smoothing Help? 6 Jun 2019 ElenaRyumina/EMO-AffectNetModel/functions/utils.py eee939c2db10a418 unverified MIT (permissive)
CBAM: Convolutional Block Attention Module 17 Jul 2018 kobiso/CBAM-keras/models/inception_resnet_v2.py de695392eae44227 unverified MIT (permissive)
CBAM: Convolutional Block Attention Module 17 Jul 2018 kobiso/CBAM-keras/models/inception_v3.py bee766c6e15044c2 unverified MIT (permissive)
CBAM: Convolutional Block Attention Module 17 Jul 2018 kobiso/CBAM-keras/models/densenet.py 2b2390bcd64e4f4f unverified MIT (permissive)
Differentiable Learning-to-Normalize via Switchable Normalization 28 Jun 2018 diogorpeixoto/SN_boxy/deps/keras_yolov3/yolo3_one_file_to_detect_them_all.py 601ca295e001cbcc unverified MIT (permissive)
Slalom: Fast, Verifiable and Private Execution of Neural Networks in Trusted Hardware 8 Jun 2018 ftramer/slalom/python/slalom/mobilenet_sep.py de695392eae44227 unverified MIT (permissive)
Dynamic Routing Between Capsules 26 Oct 2017 lalonderodney/SegCaps/densenets.py df095bccc89495e4 unverified Apache-2.0 (permissive)
Real-time Convolutional Neural Networks for Emotion and Gender Classification 20 Oct 2017 kbsriharsha/FacialEmotionAnalysis/src/preprocessing.py c0ce67208d8c54a6 unverified MIT (permissive)
Understanding Black-box Predictions via Influence Functions 14 Mar 2017 kohpangwei/influence-release/influence/imagenet_utils.py 64eb4bea3d3127c4 unverified MIT (permissive)
Xception: Deep Learning with Depthwise Separable Convolutions 7 Oct 2016 amogh7joshi/engagement-detection/util/classifyimgops.py 51d8271eca5b94ff unverified MIT (permissive)
Densely Connected Convolutional Networks 25 Aug 2016 titu1994/DenseNet/densenet.py 2b2390bcd64e4f4f unverified MIT (permissive)
Perceptual Losses for Real-Time Style Transfer and Super-Resolution 27 Mar 2016 cryu854/FastStyle/modules/vgg19.py 45d5e3cf42e43b89 ran · fixture could not drive it fingerprinted MIT (permissive)
Learning Deep Features for Discriminative Localization 14 Dec 2015 djib2011/high-res-mapping/densenet.py df095bccc89495e4 unverified MIT (permissive)
Deep Residual Learning for Image Recognition 10 Dec 2015 fchollet/deep-learning-models/imagenet_utils.py 64eb4bea3d3127c4 unverified MIT (permissive)
Deep Residual Learning for Image Recognition 10 Dec 2015 fchollet/deep-learning-models/inception_resnet_v2.py 17e355a0c16b9297 unverified MIT (permissive)
Deep Residual Learning for Image Recognition 10 Dec 2015 fchollet/deep-learning-models/mobilenet.py bee766c6e15044c2 unverified MIT (permissive)
Deep Residual Learning for Image Recognition 10 Dec 2015 fchollet/deep-learning-models/audio_conv_utils.py 73ad129b7b35ec98 unverified MIT (permissive)
Deep Residual Learning for Image Recognition 10 Dec 2015 GKalliatakis/Keras-VGG16-places365/places_utils.py 22b5e6b51db8265c unverified MIT (permissive)
Distilling the Knowledge in a Neural Network 9 Mar 2015 mckunkel/DistillingObjectDetector/models/microxception.py bee766c6e15044c2 unverified MIT (permissive)
Distilling the Knowledge in a Neural Network 9 Mar 2015 mckunkel/DistillingObjectDetector/models/squeezenet.py 9eb8ac7c03e9b11b unverified MIT (permissive)
Two-Stream Convolutional Networks for Action Recognition in Videos 9 Jun 2014 woodfrog/ActionRecognition/rnn_practice/LRCN/imagenet_utils.py b1aaeed051544edb unverified MIT (permissive)
arXiv:ijcai2025_0872 HongxinXiang/EDG/EDG/finetune_QM9_EDG.py 6b4ebeec7d6e5a73 unverified MIT (permissive)
arXiv:ijcai2025_0872 HongxinXiang/EDG/EDG/finetune_rMD17_EDG.py 8898433f6e9e723f 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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