{"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/get-assigned-file","entry":"get_assigned_file","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":5,"n_papers_ran":5,"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":1,"n_samples_ran":1,"n_samples_fingerprinted":1,"n_places":5,"n_places_pointer_only":1,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":0,"ran_fixture":0,"ran":1,"unverified":0},"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":"2403.00567","paper":"/paper/flatten-long-range-loss-landscapes-for-cross","title":"Flatten Long-Range Loss Landscapes for Cross-Domain Few-Shot Learning","date":"2024-03-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zoilsen/flor","path":"options.py","file_url":"https://github.com/zoilsen/flor/blob/HEAD/options.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"400a8db2fb9b1633","mcp_get_code":{"code_sha256":"400a8db2fb9b1633"}},{"arxiv_id":"2208.10930","paper":"/paper/fs-ban-born-again-networks-for-domain","title":"FS-BAN: Born-Again Networks for Domain Generalization Few-Shot Classification","date":"2022-08-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yunqing-me/Born-Again-FS","path":"baseline_model/options.py","file_url":"https://github.com/yunqing-me/Born-Again-FS/blob/HEAD/baseline_model/options.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"400a8db2fb9b1633","mcp_get_code":{"code_sha256":"400a8db2fb9b1633"}},{"arxiv_id":"2202.02471","paper":"/paper/few-shot-learning-as-cluster-induced-voronoi","title":"Few-shot Learning as Cluster-induced Voronoi Diagrams: A Geometric Approach","date":"2022-02-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"horsepurve/deepvoro","path":"io_utils.py","file_url":"https://github.com/horsepurve/deepvoro/blob/HEAD/io_utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"400a8db2fb9b1633","mcp_get_code":{"code_sha256":"400a8db2fb9b1633"}},{"arxiv_id":"1912.07200","paper":"/paper/a-new-benchmark-for-evaluation-of-cross","title":"A Broader Study of Cross-Domain Few-Shot Learning","date":"2019-12-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"IBM/cdfsl-benchmark","path":"io_utils.py","file_url":"https://github.com/IBM/cdfsl-benchmark/blob/HEAD/io_utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"400a8db2fb9b1633","mcp_get_code":{"code_sha256":"400a8db2fb9b1633"}},{"arxiv_id":"1905.13613","paper":"/paper/subspace-networks-for-few-shot-classification","title":"Regression Networks for Meta-Learning Few-Shot Classification","date":"2019-05-31","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ArnoutDevos/RegressionNet","path":"io_utils.py","file_url":"https://github.com/ArnoutDevos/RegressionNet/blob/HEAD/io_utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"400a8db2fb9b1633","mcp_get_code":{"code_sha256":"400a8db2fb9b1633"}}]}