{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/code/to-img","entry":"to_img","source":"Syntology graph, per-sample; not an archive number","read_at":"2026-09-24T18:15:14+00:00","claim":"Names are grouped by exact entry-name string. Same-named routines are NOT asserted to be equivalent; 'ran' means executed on a synthesized fixture, not correctness. n_samples_ran = sum of by_status over every status except 'unverified' (ran_draft_wrong and ran_fixture are failures of Syntology's instrument, not of the code); n_papers_ran = papers with at least one such sample.","status_vocabulary":{"ran_honours":"ran, honoured the contract we drafted","ran_violates":"ran, violated the contract we drafted","ran_draft_wrong":"ran; our contract draft was wrong, not the code","ran_fixture":"ran; our fixture could not drive it","ran":"ran on a synthesized input","unverified":"unverified (harvested, no recorded run)"},"n_papers":8,"n_papers_ran":2,"units":"n_samples, n_samples_ran, n_samples_fingerprinted and by_status count distinct code bodies (code_sha256); n_places and n_places_pointer_only count places, one per (paper, code body) pair, which is also the unit of the samples list","n_samples":6,"n_samples_ran":2,"n_samples_fingerprinted":2,"n_places":8,"n_places_pointer_only":2,"by_status":{"ran_honours":1,"ran_violates":0,"ran_draft_wrong":0,"ran_fixture":1,"ran":0,"unverified":4},"syntology":{"atlas_url":null,"mcp":null,"mcp_per_sample":{"tool":"get_code","arguments_in":"samples[].mcp_get_code"},"developers":"https://syntology.ai/developers"},"samples":[{"arxiv_id":"2110.03091","paper":"/paper/improving-fractal-pre-training","title":"Improving Fractal Pre-training","date":"2021-10-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"catalys1/fractal-pretraining","path":"fractal_learning/training/makegif.py","file_url":"https://github.com/catalys1/fractal-pretraining/blob/HEAD/fractal_learning/training/makegif.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"15b863dcd6f5736e","mcp_get_code":{"code_sha256":"15b863dcd6f5736e"}},{"arxiv_id":"2107.04556","paper":"/paper/deep-learning-for-reduced-order-modelling-and","title":"Deep Learning for Reduced Order Modelling and Efficient Temporal Evolution of Fluid Simulations","date":"2021-07-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"pranshupant/DL-ROM","path":"code/utils.py","file_url":"https://github.com/pranshupant/DL-ROM/blob/HEAD/code/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"062ea17c7d4e91b1","mcp_get_code":{"code_sha256":"062ea17c7d4e91b1"}},{"arxiv_id":"2010.00679","paper":"/paper/implicit-rank-minimizing-autoencoder","title":"Implicit Rank-Minimizing Autoencoder","date":"2020-10-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"dibyadas/IRMAE","path":"implementation_irmae.py","file_url":"https://github.com/dibyadas/IRMAE/blob/HEAD/implementation_irmae.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"76c00671024762c0","mcp_get_code":{"code_sha256":"76c00671024762c0"}},{"arxiv_id":"2009.13333","paper":"/paper/group-whitening-balancing-learning-efficiency","title":"Group Whitening: Balancing Learning Efficiency and Representational Capacity","date":"2020-09-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"huangleiBuaa/GroupWhitening","path":"classification/Mnist/mnist.py","file_url":"https://github.com/huangleiBuaa/GroupWhitening/blob/HEAD/classification/Mnist/mnist.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-2-Clause","inline_ok":true,"code_sha256_prefix":"cacaa2915e934d81","mcp_get_code":{"code_sha256":"cacaa2915e934d81"}},{"arxiv_id":"2003.12327","paper":"/paper/an-investigation-into-the-stochasticity-of","title":"An Investigation into the Stochasticity of Batch Whitening","date":"2020-03-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"huangleiBuaa/StochasticityBW","path":"SBW_Classification_PyTorch/Mnist/mnist.py","file_url":"https://github.com/huangleiBuaa/StochasticityBW/blob/HEAD/SBW_Classification_PyTorch/Mnist/mnist.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-2-Clause","inline_ok":true,"code_sha256_prefix":"cacaa2915e934d81","mcp_get_code":{"code_sha256":"cacaa2915e934d81"}},{"arxiv_id":"1912.04958","paper":"/paper/analyzing-and-improving-the-image-quality-of","title":"Analyzing and Improving the Image Quality of StyleGAN","date":"2019-12-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"simplerick/stylegan2_pytorch","path":"misc.py","file_url":"https://github.com/simplerick/stylegan2_pytorch/blob/HEAD/misc.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"d4bb2865508c3d4b","mcp_get_code":{"code_sha256":"d4bb2865508c3d4b"}},{"arxiv_id":"1905.01639","paper":"/paper/deep-video-inpainting","title":"Deep Video Inpainting","date":"2019-05-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mcahny/Deep-Video-Inpainting","path":"demo_vi.py","file_url":"https://github.com/mcahny/Deep-Video-Inpainting/blob/HEAD/demo_vi.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"12ab122eb2417314","mcp_get_code":{"code_sha256":"12ab122eb2417314"}},{"arxiv_id":"1904.03441","paper":"/paper/iterative-normalization-beyond","title":"Iterative Normalization: Beyond Standardization towards Efficient Whitening","date":"2019-04-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"huangleiBuaa/IterNorm-pytorch","path":"cifar10/mnist.py","file_url":"https://github.com/huangleiBuaa/IterNorm-pytorch/blob/HEAD/cifar10/mnist.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-2-Clause","inline_ok":true,"code_sha256_prefix":"cacaa2915e934d81","mcp_get_code":{"code_sha256":"cacaa2915e934d81"}}]}