{"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/default-flist-reader","entry":"default_flist_reader","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":7,"n_papers_ran":4,"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":5,"n_samples_ran":2,"n_samples_fingerprinted":0,"n_places":7,"n_places_pointer_only":1,"by_status":{"ran_honours":0,"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":"2408.14961","paper":"/paper/cvpt-cross-attention-help-visual-prompt","title":"CVPT: Cross-Attention help Visual Prompt Tuning adapt visual task","date":"2024-08-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"xlgsyzp/cvpt","path":"dataset.py","file_url":"https://github.com/xlgsyzp/cvpt/blob/HEAD/dataset.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"a2d01766b58dccb1","mcp_get_code":{"code_sha256":"a2d01766b58dccb1"}},{"arxiv_id":"2406.16231","paper":"/paper/gradual-divergence-for-seamless-adaptation-a","title":"Gradual Divergence for Seamless Adaptation: A Novel Domain Incremental Learning Method","date":"2024-06-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"neurai-lab/dare","path":"datasets/domain_net.py","file_url":"https://github.com/neurai-lab/dare/blob/HEAD/datasets/domain_net.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"3656b4165dac134a","mcp_get_code":{"code_sha256":"3656b4165dac134a"}},{"arxiv_id":"2310.06234","paper":"/paper/efficient-adaptation-of-large-vision-1","title":"Efficient Adaptation of Large Vision Transformer via Adapter Re-Composing","date":"2023-10-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"DavidYanAnDe/ARC","path":"Data_process/VTAB_loader.py","file_url":"https://github.com/DavidYanAnDe/ARC/blob/HEAD/Data_process/VTAB_loader.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"a2d01766b58dccb1","mcp_get_code":{"code_sha256":"a2d01766b58dccb1"}},{"arxiv_id":"2301.12689","paper":"/paper/edge-guided-multi-domain-rgb-to-tir-image","title":"Edge-guided Multi-domain RGB-to-TIR image Translation for Training Vision Tasks with Challenging Labels","date":"2023-01-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"rpmsnu/srgb-tir","path":"data.py","file_url":"https://github.com/rpmsnu/srgb-tir/blob/HEAD/data.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"e1819f259557e9bc","mcp_get_code":{"code_sha256":"e1819f259557e9bc"}},{"arxiv_id":"2207.07517","paper":"/paper/on-the-usefulness-of-deep-ensemble-diversity","title":"On the Usefulness of Deep Ensemble Diversity for Out-of-Distribution Detection","date":"2022-07-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"guoxoug/ens-div-ood-detect","path":"utils/data_utils.py","file_url":"https://github.com/guoxoug/ens-div-ood-detect/blob/HEAD/utils/data_utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":true,"code_sha256_prefix":"64fc75cbc3ced233","mcp_get_code":{"code_sha256":"64fc75cbc3ced233"}},{"arxiv_id":"1710.03463","paper":"/paper/learning-to-generalize-meta-learning-for","title":"Learning to Generalize: Meta-Learning for Domain Generalization","date":"2017-10-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Pulkit-Khandelwal/mldg","path":"data_reader.py","file_url":"https://github.com/Pulkit-Khandelwal/mldg/blob/HEAD/data_reader.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":"a2d01766b58dccb1","mcp_get_code":{"code_sha256":"a2d01766b58dccb1"}},{"arxiv_id":"Liu_GEN_Pushing_the_Limits_of_Softmax-Based_Out-of-Distribution_Detection_CVPR_2023_paper","paper":null,"title":"arXiv:Liu_GEN_Pushing_the_Limits_of_Softmax-Based_Out-of-Distribution_Detection_CVPR_2023_paper","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"XixiLiu95/GEN","path":"list_dataset.py","file_url":"https://github.com/XixiLiu95/GEN/blob/HEAD/list_dataset.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":"c0cf655b5643c4ac","mcp_get_code":{"code_sha256":"c0cf655b5643c4ac"}}]}