{"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/process-img","entry":"process_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":6,"n_papers_ran":3,"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":3,"n_samples_fingerprinted":1,"n_places":6,"n_places_pointer_only":1,"by_status":{"ran_honours":1,"ran_violates":0,"ran_draft_wrong":0,"ran_fixture":0,"ran":2,"unverified":3},"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":"2406.05857","paper":"/paper/self-supervised-adversarial-training-of","title":"Self-supervised Adversarial Training of Monocular Depth Estimation against Physical-World Attacks","date":"2024-06-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Bob-cheng/DepthModelHardening","path":"image_preprocess.py","file_url":"https://github.com/Bob-cheng/DepthModelHardening/blob/HEAD/image_preprocess.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"15fe5d7084a8f164","mcp_get_code":{"code_sha256":"15fe5d7084a8f164"}},{"arxiv_id":"2312.13286","paper":"/paper/generative-multimodal-models-are-in-context","title":"Generative Multimodal Models are In-Context Learners","date":"2023-12-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"baaivision/emu","path":"Emu1/mm_eval/models/emu.py","file_url":"https://github.com/baaivision/emu/blob/HEAD/Emu1/mm_eval/models/emu.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"fe8b1e61f82d575d","mcp_get_code":{"code_sha256":"fe8b1e61f82d575d"}},{"arxiv_id":"2309.00216","paper":"/paper/human-inspired-facial-sketch-synthesis-with","title":"Human-Inspired Facial Sketch Synthesis with Dynamic Adaptation","date":"2023-09-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"AiArt-HDU/HIDA","path":"SIFID/sifid_score_unet.py","file_url":"https://github.com/AiArt-HDU/HIDA/blob/HEAD/SIFID/sifid_score_unet.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"9700906f2822ce75","mcp_get_code":{"code_sha256":"9700906f2822ce75"}},{"arxiv_id":"1809.05630","paper":"/paper/towards-better-interpretability-in-deep-q","title":"Towards Better Interpretability in Deep Q-Networks","date":"2018-09-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"maraghuram/I-DQN","path":"src/double_dqn.py","file_url":"https://github.com/maraghuram/I-DQN/blob/HEAD/src/double_dqn.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"well_formed","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"0affebd843c4c084","mcp_get_code":{"code_sha256":"0affebd843c4c084"}},{"arxiv_id":"1605.09304","paper":"/paper/synthesizing-the-preferred-inputs-for-neurons","title":"Synthesizing the preferred inputs for neurons in neural networks via deep generator networks","date":"2016-05-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ndey96/deep-generator-network","path":"gen_init_compare.py","file_url":"https://github.com/ndey96/deep-generator-network/blob/HEAD/gen_init_compare.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"d73ff14130ee6a57","mcp_get_code":{"code_sha256":"d73ff14130ee6a57"}},{"arxiv_id":"Wu_Domain_Separation_Graph_Neural_Networks_for_Saliency_Object_Ranking_CVPR_2024_paper","paper":null,"title":"arXiv:Wu_Domain_Separation_Graph_Neural_Networks_for_Saliency_Object_Ranking_CVPR_2024_paper","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"Wu-ZJ/DSGNN","path":"demo/video_gpuaccel_demo.py","file_url":"https://github.com/Wu-ZJ/DSGNN/blob/HEAD/demo/video_gpuaccel_demo.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"df955ed86df42716","mcp_get_code":{"code_sha256":"df955ed86df42716"}}]}