{"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/reshape-transform","entry":"reshape_transform","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":13,"n_papers_ran":9,"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":8,"n_samples_ran":4,"n_samples_fingerprinted":1,"n_places":13,"n_places_pointer_only":6,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":1,"ran_fixture":0,"ran":3,"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":"2608.24597","paper":"/paper/arxiv-2608-24597","title":"Taming foundation model with invariance-oriented pre-training for broad-spectrum EEG analysis across signal-level, brain-state, and brain-health tasks","date":null,"month_inferred_from_arxiv_id":"2026-08","title_source":"syntology","repo":"eeyhsong/EEG-Conformer","path":"visualization/CAT.py","file_url":"https://github.com/eeyhsong/EEG-Conformer/blob/HEAD/visualization/CAT.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"GPL-3.0","inline_ok":false,"code_sha256_prefix":"4ed0c3453d83c7cf","mcp_get_code":{"code_sha256":"4ed0c3453d83c7cf"}},{"arxiv_id":"2509.21247","paper":"/paper/arxiv-2509-21247","title":"Learning to Look: Cognitive Attention Alignment with Vision-Language Models","date":null,"month_inferred_from_arxiv_id":"2025-09","title_source":"syntology","repo":"ryanlyang/LearningToLook","path":"code/WeCLIPPlus/WeCLIP_Plus/model_attn_aff_coco.py","file_url":"https://github.com/ryanlyang/LearningToLook/blob/HEAD/code/WeCLIPPlus/WeCLIP_Plus/model_attn_aff_coco.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"5bd180a934a83565","mcp_get_code":{"code_sha256":"5bd180a934a83565"}},{"arxiv_id":"2406.11189","paper":"/paper/frozen-clip-a-strong-backbone-for-weakly-1","title":"Frozen CLIP: A Strong Backbone for Weakly Supervised Semantic Segmentation","date":"2024-06-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zbf1991/WeCLIP","path":"WeCLIP+/WeCLIP_Plus/model_attn_aff_coco.py","file_url":"https://github.com/zbf1991/WeCLIP/blob/HEAD/WeCLIP%2B/WeCLIP_Plus/model_attn_aff_coco.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"5bd180a934a83565","mcp_get_code":{"code_sha256":"5bd180a934a83565"}},{"arxiv_id":"2311.16450","paper":"/paper/typhoon-intensity-prediction-with-vision","title":"Typhoon Intensity Prediction with Vision Transformer","date":"2023-11-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"chen-huanxin/Tint","path":"utils/grad_cam_resnet.py","file_url":"https://github.com/chen-huanxin/Tint/blob/HEAD/utils/grad_cam_resnet.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"8120aae8b01d5c16","mcp_get_code":{"code_sha256":"8120aae8b01d5c16"}},{"arxiv_id":"2311.09084","paper":"/paper/contrastive-transformer-learning-with","title":"Contrastive Transformer Learning with Proximity Data Generation for Text-Based Person Search","date":"2023-11-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hcplab-sysu/personsearch-ctlg","path":"grad_cam_vis.py","file_url":"https://github.com/hcplab-sysu/personsearch-ctlg/blob/HEAD/grad_cam_vis.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"574e71d15f0a0bc4","mcp_get_code":{"code_sha256":"574e71d15f0a0bc4"}},{"arxiv_id":"2310.13026","paper":"/paper/weakly-supervised-semantic-segmentation-with-2","title":"Weakly-Supervised Semantic Segmentation with Image-Level Labels: from Traditional Models to Foundation Models","date":"2023-10-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zhaozhengchen/sam_wsss","path":"sam_text_input_coco.py","file_url":"https://github.com/zhaozhengchen/sam_wsss/blob/HEAD/sam_text_input_coco.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"5bd180a934a83565","mcp_get_code":{"code_sha256":"5bd180a934a83565"}},{"arxiv_id":"2306.03310","paper":"/paper/libero-benchmarking-knowledge-transfer-for","title":"LIBERO: Benchmarking Knowledge Transfer for Lifelong Robot Learning","date":"2023-06-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"HarryLui98/DMPEL","path":"libero/lifelong/models/bc_foundation_dmpel_policy.py","file_url":"https://github.com/HarryLui98/DMPEL/blob/HEAD/libero/lifelong/models/bc_foundation_dmpel_policy.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"002c05cdb4cfb345","mcp_get_code":{"code_sha256":"002c05cdb4cfb345"}},{"arxiv_id":"2212.09506","paper":"/paper/clip-is-also-an-efficient-segmenter-a-text","title":"CLIP is Also an Efficient Segmenter: A Text-Driven Approach for Weakly Supervised Semantic Segmentation","date":"2022-12-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"linyq2117/clip-es","path":"generate_cams_voc12.py","file_url":"https://github.com/linyq2117/clip-es/blob/HEAD/generate_cams_voc12.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"5bd180a934a83565","mcp_get_code":{"code_sha256":"5bd180a934a83565"}},{"arxiv_id":"2207.09684","paper":"/paper/on-the-versatile-uses-of-partial-distance","title":"On the Versatile Uses of Partial Distance Correlation in Deep Learning","date":"2022-07-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zhenxingjian/partial_distance_correlation","path":"Partial_Distance_Correlation/main_CAM.py","file_url":"https://github.com/zhenxingjian/partial_distance_correlation/blob/HEAD/Partial_Distance_Correlation/main_CAM.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"2af53fce5490aa77","mcp_get_code":{"code_sha256":"2af53fce5490aa77"}},{"arxiv_id":"2202.09844","paper":"/paper/sparsity-winning-twice-better-robust-1","title":"Sparsity Winning Twice: Better Robust Generalization from More Efficient Training","date":"2022-02-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"vita-group/sparsity-win-robust-generalization","path":"grad_carm.py","file_url":"https://github.com/vita-group/sparsity-win-robust-generalization/blob/HEAD/grad_carm.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"c10449a1b159ff70","mcp_get_code":{"code_sha256":"c10449a1b159ff70"}},{"arxiv_id":"ijcai2023_0504","paper":null,"title":"arXiv:ijcai2023_0504","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"Markin-Wang/CLEViT","path":"extract_features.py","file_url":"https://github.com/Markin-Wang/CLEViT/blob/HEAD/extract_features.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":false,"code_sha256_prefix":"dc43fbe761e75812","mcp_get_code":{"code_sha256":"dc43fbe761e75812"}},{"arxiv_id":"aaai_28139","paper":null,"title":"arXiv:aaai_28139","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"linyq2117/TagCLIP","path":"CLIP-ES/generate_cams_coco.py","file_url":"https://github.com/linyq2117/TagCLIP/blob/HEAD/CLIP-ES/generate_cams_coco.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"5bd180a934a83565","mcp_get_code":{"code_sha256":"5bd180a934a83565"}},{"arxiv_id":"Lin_CLIP_Is_Also_an_Efficient_Segmenter_A_Text-Driven_Approach_for_CVPR_2023_paper","paper":null,"title":"arXiv:Lin_CLIP_Is_Also_an_Efficient_Segmenter_A_Text-Driven_Approach_for_CVPR_2023_paper","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"linyq2117/CLIP-ES","path":"generate_cams_coco14.py","file_url":"https://github.com/linyq2117/CLIP-ES/blob/HEAD/generate_cams_coco14.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"5bd180a934a83565","mcp_get_code":{"code_sha256":"5bd180a934a83565"}}]}