{"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-classnames","entry":"get_classnames","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":1,"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":3,"n_samples_ran":1,"n_samples_fingerprinted":0,"n_places":7,"n_places_pointer_only":4,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":0,"ran_fixture":0,"ran":1,"unverified":2},"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":"2411.19757","paper":"/paper/dual-risk-minimization-towards-next-level","title":"Dual Risk Minimization: Towards Next-Level Robustness in Fine-tuning Zero-Shot Models","date":"2024-11-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"vaynexie/DRM","path":"src/datasets/imagenet_classnames.py","file_url":"https://github.com/vaynexie/DRM/blob/HEAD/src/datasets/imagenet_classnames.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"8d5ec0683c47758f","mcp_get_code":{"code_sha256":"8d5ec0683c47758f"}},{"arxiv_id":"2411.06966","paper":"/paper/robust-fine-tuning-of-zero-shot-models-via","title":"Robust Fine-tuning of Zero-shot Models via Variance Reduction","date":"2024-11-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"BeierZhu/VRF","path":"src/datasets/imagenet_classnames.py","file_url":"https://github.com/BeierZhu/VRF/blob/HEAD/src/datasets/imagenet_classnames.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"3903d266387afea6","mcp_get_code":{"code_sha256":"3903d266387afea6"}},{"arxiv_id":"2410.03955","paper":"/paper/model-developmental-safety-a-safety-centric","title":"A Retention-Centric Framework for Continual Learning with Guaranteed Model Developmental Safety","date":"2024-10-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ganglii/devsafety","path":"src/datasets/bdd100k_classnames.py","file_url":"https://github.com/ganglii/devsafety/blob/HEAD/src/datasets/bdd100k_classnames.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"43e90e164020c15a","mcp_get_code":{"code_sha256":"43e90e164020c15a"}},{"arxiv_id":"2406.03345","paper":"/paper/feature-contamination-neural-networks-learn","title":"Feature Contamination: Neural Networks Learn Uncorrelated Features and Fail to Generalize","date":"2024-06-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"trzhang0116/feature-contamination","path":"datasets/imagenet_classnames.py","file_url":"https://github.com/trzhang0116/feature-contamination/blob/HEAD/datasets/imagenet_classnames.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"3903d266387afea6","mcp_get_code":{"code_sha256":"3903d266387afea6"}},{"arxiv_id":"2403.11549","paper":"/paper/boosting-continual-learning-of-vision","title":"Boosting Continual Learning of Vision-Language Models via Mixture-of-Experts Adapters","date":"2024-03-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jiazuoyu/moe-adapters4cl","path":"cil/datasets_ref/imagenet_classnames.py","file_url":"https://github.com/jiazuoyu/moe-adapters4cl/blob/HEAD/cil/datasets_ref/imagenet_classnames.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"3903d266387afea6","mcp_get_code":{"code_sha256":"3903d266387afea6"}},{"arxiv_id":"2403.10245","paper":"/paper/coleclip-open-domain-continual-learning-via","title":"CoLeCLIP: Open-Domain Continual Learning via Joint Task Prompt and Vocabulary Learning","date":"2024-03-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"YukunLi99/CoLeCLIP","path":"datasets/imagenet_classnames.py","file_url":"https://github.com/YukunLi99/CoLeCLIP/blob/HEAD/datasets/imagenet_classnames.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"3903d266387afea6","mcp_get_code":{"code_sha256":"3903d266387afea6"}},{"arxiv_id":"2312.02546","paper":"/paper/machine-vision-therapy-multimodal-large","title":"Machine Vision Therapy: Multimodal Large Language Models Can Enhance Visual Robustness via Denoising In-Context Learning","date":"2023-12-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"tmllab/2024_icml_dicl","path":"ImageNet_datasets/imagenet_classnames.py","file_url":"https://github.com/tmllab/2024_icml_dicl/blob/HEAD/ImageNet_datasets/imagenet_classnames.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"3903d266387afea6","mcp_get_code":{"code_sha256":"3903d266387afea6"}}]}