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generate_image

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

generate_image appears in the code Syntology harvested for 15 papers, as 14 distinct code bodies found in 19 places (a place is one code body under one paper). At least one of them ran in 4 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 generate_image 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 14 distinct code bodies named generate_image; 10 are unverified. One tile per status, in the site's fixed vocabulary, each code body counted once:

1ran · honoured contract
0ran · violated contract
1ran · our draft was wrong
0ran · fixture could not drive it
2ran
10unverified
0fingerprinted

Licence is a property of each copy, so it is counted per place: 10 of the 19 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

15 papers shown of 15, newest first; 19 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 3 papers added by Syntology; 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
Do Video Generators Track the World Across Segments? A Benchmark and Method for World-State Reasoning in Video Continuation added by Syntology 2026-09 (from id) AMAP-ML/StateAgent/generators/image_generator.py 8b209605af442037 unverified MIT (permissive)
Learning to Refine: Spectral-Decoupled Iterative Refinement Framework for Precipitation Nowcasting added by Syntology 2026-06 (from id) RuntimeWarning/SDIR/helpers/visualization.py 4f5fe11aa5afcdb3 ran no licence file found · pointer only
SPoRC-VIST: A Benchmark for Evaluating Generative Natural Narrative in Vision-Language Models added by Syntology 2026-01 (from id) Yunlin-Zeng/visual-podcast-VLM/scripts_bedrock/2025-11-24_generate_images.py 57addbe111d83710 unverified licence not identified · pointer only
MARVEL-40M+: Multi-Level Visual Elaboration for High-Fidelity Text-to-3D Content Creation 26 Nov 2024 SadilKhan/MARVEL-FX3D/pipeline/imagegen.py f3aeb0585dbdedf2 unverified MIT (permissive)
Leveraging Hallucinations to Reduce Manual Prompt Dependency in Promptable Segmentation 27 Aug 2024 lwpyh/ProMaC_code/utils_mllm.py 4379261942974adb unverified MIT (permissive)
Measuring Agreeableness Bias in Multimodal Models 17 Aug 2024 jasonlim131/looksRdeceiving/python-src/generate_vmmlu.py f2f7c5f5e999d421 ran · honoured contract MIT (permissive)
Safety Fine-Tuning at (Almost) No Cost: A Baseline for Vision Large Language Models 3 Feb 2024 ys-zong/vlguard/gpt4_evaluator.py 97f63c0705ddb3a8 unverified no licence file found · pointer only
Hacking Generative Models with Differentiable Network Bending 7 Oct 2023 GAldegheri/gan-bending/utils.py 820da4fab1a61ac6 ran no licence file found · pointer only
ObjectLab: Automated Diagnosis of Mislabeled Images in Object Detection Data 2 Sep 2023 cleanlab/cleanlab/tests/test_object_detection.py 5b9fde645fe4b803 ran · our draft was wrong Apache-2.0 (permissive)
Right for the Right Reasons: Training Differentiable Models by Constraining their Explanations 10 Mar 2017 dtak/rrr/rrr/toy_colors.py c106f94602bc64a0 unverified MIT (permissive)
Synthesizing the preferred inputs for neurons in neural networks via deep generator networks 30 May 2016 identical code first harvested elsewhere b43d90577686455e unverified licence of this copy not recorded
Synthesizing the preferred inputs for neurons in neural networks via deep generator networks 30 May 2016 identical code first harvested elsewhere f4b8e2487402d23c unverified licence of this copy not recorded
Generating Images with Perceptual Similarity Metrics based on Deep Networks 8 Feb 2016 KamitaniLab/cnnpref/cnnpref/prefer_img_gd.py b43d90577686455e unverified MIT (permissive)
Generating Images with Perceptual Similarity Metrics based on Deep Networks 8 Feb 2016 KamitaniLab/cnnpref/cnnpref/prefer_img_dgn_gd.py f4b8e2487402d23c unverified MIT (permissive)
Understanding Neural Networks Through Deep Visualization 22 Jun 2015 identical code first harvested elsewhere b43d90577686455e unverified licence of this copy not recorded
Understanding Neural Networks Through Deep Visualization 22 Jun 2015 identical code first harvested elsewhere f4b8e2487402d23c unverified licence of this copy not recorded
Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps 20 Dec 2013 identical code first harvested elsewhere b43d90577686455e unverified licence of this copy not recorded
Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps 20 Dec 2013 KamitaniLab/cnnpref/cnnpref/prefer_img_lbfgs.py a70f1a3307211511 unverified MIT (permissive)
arXiv:2025.findings-emnlp.49 27yw/GenPilot/ttpo.py 22d39bbf2494b1eb unverified no licence file found · pointer only

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