{"about":{"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.","site":"https://codewithpapers.app","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","syntology":{"site":"https://syntology.ai","developers":"https://syntology.ai/developers","mcp":{"server":"https://syntology.ai/mcp","transport":"streamable-http","server_card":"https://syntology.ai/.well-known/mcp/server-card.json","auth":{"type":"trial token, no account","trial_token":"https://syntology.ai/api/oauth/trial/token","method":"POST","docs":"https://syntology.ai/developers"}},"have":"https://syntology.ai/api/graph/have?x=<method, arXiv id or title> (free, answers coverage only)","paper_base":"https://syntology.ai/paper/","atlas_base":"https://app.syntology.ai/?focus="},"machine_readable":[{"url":"https://codewithpapers.app/llms.txt","what":"the machine catalog: every machine-readable file, counted"},{"url":"https://codewithpapers.app/index/manifest.json","what":"paper-to-code index by arXiv id, with Syntology's counts"},{"url":"https://codewithpapers.app/search/manifest.json","what":"site search index (titles, authors) and its files"},{"url":"https://codewithpapers.app/download","what":"bulk files: Syntology's layer, described there"},{"url":"https://codewithpapers.app/build_manifest.json","what":"the build record: inputs, counts, exclusions, probes"}]},"url":"/dataset/caltech-101/papers/ran/1","list_of":"/dataset/caltech-101","dataset":"Caltech-101","archive":{"snapshot":"2025-07-28"},"syntology_read_at":"2026-09-28T10:30:06+00:00","key_notes":{"samples_ran_checked":"legacy name, kept unchanged so existing readers do not break: it counts the samples that ran with no instrument failure (honoured, violated, and ran with no contract checked); it does not mean a contract was checked, and the pages print it as 'K with no instrument failure', not 'K checked'","samples_constructed":"a sub-count of the samples that ran, never subtracted from them and never a failure: an executed sample whose run returned an instance of its own class (fixture_out_type equals the entry name): the run built an object and did not compute a result (Syntology's RAN record, counts.constructed)"},"order":"ran","order_definition":"only papers where Syntology ran at least one harvested sample; date (newest first), ties by arXiv id","caption":"We ran code from the paper's repository; we did not run it on this dataset or check it against this dataset's benchmarks.","absence":"A paper missing from this list is not a recorded non-run: it may have no arXiv id, no harvested code, or only samples that have not run yet.","population":"every paper with a leaderboard row on this dataset's benchmarks (the benchmark-backed subset): the archive's own papers-using-this-dataset list was never published, so this is not that list; num_papers_in_archive is the archive's own count","page":1,"pages_in_order":1,"rows_per_page":100,"rows":[1,19],"of":19,"counts":{"papers_with_a_benchmark_row":38,"with_a_code_link":35,"where_syntology_ran_a_sample":19,"not_listed_spam_title":0,"listed":38,"listed_where_code_ran":19,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":18,"every_run_a_failure_of_syntologys_instrument":1,"listed_with_a_run_with_no_instrument_failure":18,"listed_every_run_a_failure_of_syntologys_instrument":1,"filter":{"states":["a run with no instrument failure","any run, instrument failures included"],"default":"a run with no instrument failure","note":"on the 'only where code ran' pages the default hides, in the browser, the rows where every run was a failure of Syntology's instrument; the second state shows them again. Rows are hidden, never re-ordered; these twins list every row"}},"definition":"distinct papers with at least one leaderboard row on this dataset's benchmarks; 'where Syntology ran a sample' counts papers with at least one harvested sample that ran, which is not a correctness claim"},"first_page":"/dataset/caltech-101/papers/ran/1","prev":null,"next":null,"papers":[{"paper":"/paper/let-go-of-your-labels-with-unsupervised-1","slug":"let-go-of-your-labels-with-unsupervised-1","title":"Let Go of Your Labels with Unsupervised Transfer","date":"2024-06-11","arxiv_id":"2406.07236","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":4,"samples_ran":3,"samples_constructed":0,"samples_ran_checked":1,"samples_ran_instrument_failed":2,"samples_unverified":1,"pointer_only_for_licence":4,"official":{"repos":["mlbio-epfl/turtle"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/let-go-of-your-labels-with-unsupervised-1#ran","syntology_url":"https://syntology.ai/paper/2406.07236","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.07236"}}}},{"paper":"/paper/label-propagation-for-zero-shot","slug":"label-propagation-for-zero-shot","title":"Label Propagation for Zero-shot Classification with Vision-Language Models","date":"2024-04-05","arxiv_id":"2404.04072","rows_on_this_dataset":3,"code_links":1,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":2,"samples_ran":1,"samples_constructed":0,"samples_ran_checked":1,"samples_ran_instrument_failed":0,"samples_unverified":1,"pointer_only_for_licence":0,"official":{"repos":["vladan-stojnic/zlap"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/label-propagation-for-zero-shot#ran","syntology_url":"https://syntology.ai/paper/2404.04072","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2404.04072"}}}},{"paper":"/paper/prompt-learning-via-meta-regularization","slug":"prompt-learning-via-meta-regularization","title":"Prompt Learning via Meta-Regularization","date":"2024-04-01","arxiv_id":"2404.00851","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":7,"samples_ran":4,"samples_constructed":0,"samples_ran_checked":1,"samples_ran_instrument_failed":3,"samples_unverified":3,"pointer_only_for_licence":2,"official":{"repos":["mlvlab/prometar"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":3,"ran_from_kinds":["official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/prompt-learning-via-meta-regularization#ran","syntology_url":"https://syntology.ai/paper/2404.00851","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2404.00851"}}}},{"paper":"/paper/learning-hierarchical-prompt-with-structured","slug":"learning-hierarchical-prompt-with-structured","title":"Learning Hierarchical Prompt with Structured Linguistic Knowledge for Vision-Language Models","date":"2023-12-11","arxiv_id":"2312.06323","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":7,"samples_ran":4,"samples_constructed":0,"samples_ran_checked":1,"samples_ran_instrument_failed":3,"samples_unverified":3,"pointer_only_for_licence":5,"official":{"repos":["vill-lab/2024-aaai-hpt"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":3,"ran_from_kinds":["official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/learning-hierarchical-prompt-with-structured#ran","syntology_url":"https://syntology.ai/paper/2312.06323","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2312.06323"}}}},{"paper":"/paper/dept-decoupled-prompt-tuning","slug":"dept-decoupled-prompt-tuning","title":"DePT: Decoupled Prompt Tuning","date":"2023-09-14","arxiv_id":"2309.07439","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":8,"samples_ran":4,"samples_constructed":0,"samples_ran_checked":1,"samples_ran_instrument_failed":3,"samples_unverified":4,"pointer_only_for_licence":8,"official":{"repos":["koorye/dept"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":4,"ran_from_kinds":["official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/dept-decoupled-prompt-tuning#ran","syntology_url":"https://syntology.ai/paper/2309.07439","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2309.07439"}}}},{"paper":"/paper/read-only-prompt-optimization-for-vision","slug":"read-only-prompt-optimization-for-vision","title":"Read-only Prompt Optimization for Vision-Language Few-shot Learning","date":"2023-08-29","arxiv_id":"2308.14960","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":6,"samples_ran":4,"samples_constructed":0,"samples_ran_checked":1,"samples_ran_instrument_failed":3,"samples_unverified":2,"pointer_only_for_licence":2,"official":{"repos":["mlvlab/rpo"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":2,"ran_from_kinds":["official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/read-only-prompt-optimization-for-vision#ran","syntology_url":"https://syntology.ai/paper/2308.14960","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2308.14960"}}}},{"paper":"/paper/self-regulating-prompts-foundational-model","slug":"self-regulating-prompts-foundational-model","title":"Self-regulating Prompts: Foundational Model Adaptation without Forgetting","date":"2023-07-13","arxiv_id":"2307.06948","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":21,"samples_ran":17,"samples_constructed":0,"samples_ran_checked":13,"samples_ran_instrument_failed":4,"samples_unverified":4,"pointer_only_for_licence":3,"official":{"repos":["muzairkhattak/promptsrc"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":2,"ran_from_kinds":["listed","official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/self-regulating-prompts-foundational-model#ran","syntology_url":"https://syntology.ai/paper/2307.06948","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2307.06948"}}}},{"paper":"/paper/consistency-guided-prompt-learning-for-vision","slug":"consistency-guided-prompt-learning-for-vision","title":"Consistency-guided Prompt Learning for Vision-Language Models","date":"2023-06-01","arxiv_id":"2306.01195","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":7,"samples_ran":4,"samples_constructed":0,"samples_ran_checked":1,"samples_ran_instrument_failed":3,"samples_unverified":3,"pointer_only_for_licence":4,"official":{"repos":["shuvenduroy/coprompt"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":3,"ran_from_kinds":["official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/consistency-guided-prompt-learning-for-vision#ran","syntology_url":"https://syntology.ai/paper/2306.01195","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2306.01195"}}}},{"paper":"/paper/maple-multi-modal-prompt-learning","slug":"maple-multi-modal-prompt-learning","title":"MaPLe: Multi-modal Prompt Learning","date":"2022-10-06","arxiv_id":"2210.03117","rows_on_this_dataset":1,"code_links":3,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":6,"samples_ran":4,"samples_constructed":0,"samples_ran_checked":1,"samples_ran_instrument_failed":3,"samples_unverified":2,"pointer_only_for_licence":2,"official":{"repos":["muzairkhattak/multimodal-prompt-learning"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":2,"ran_from_kinds":["official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/maple-multi-modal-prompt-learning#ran","syntology_url":"https://syntology.ai/paper/2210.03117","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.03117"}}}},{"paper":"/paper/bamboo-building-mega-scale-vision-dataset","slug":"bamboo-building-mega-scale-vision-dataset","title":"Bamboo: Building Mega-Scale Vision Dataset Continually with Human-Machine Synergy","date":"2022-03-15","arxiv_id":"2203.07845","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":3,"samples_ran":3,"samples_constructed":0,"samples_ran_checked":3,"samples_ran_instrument_failed":0,"samples_unverified":0,"pointer_only_for_licence":3,"official":{"repos":["davidzhangyuanhan/bamboo","zhangyuanhan-ai/bamboo"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":0,"ran_from_kinds":["official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/bamboo-building-mega-scale-vision-dataset#ran","syntology_url":"https://syntology.ai/paper/2203.07845","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.07845"}}}},{"paper":"/paper/conditional-prompt-learning-for-vision","slug":"conditional-prompt-learning-for-vision","title":"Conditional Prompt Learning for Vision-Language Models","date":"2022-03-10","arxiv_id":"2203.05557","rows_on_this_dataset":1,"code_links":12,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":6,"samples_ran":4,"samples_constructed":0,"samples_ran_checked":1,"samples_ran_instrument_failed":3,"samples_unverified":2,"pointer_only_for_licence":3,"official":{"repos":["kaiyangzhou/coop"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/conditional-prompt-learning-for-vision#ran","syntology_url":"https://syntology.ai/paper/2203.05557","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.05557"}}}},{"paper":"/paper/self-supervised-learning-by-estimating-twin-1","slug":"self-supervised-learning-by-estimating-twin-1","title":"Self-Supervised Learning by Estimating Twin Class Distributions","date":"2021-10-14","arxiv_id":"2110.07402","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":15,"samples_ran":9,"samples_constructed":0,"samples_ran_checked":5,"samples_ran_instrument_failed":4,"samples_unverified":6,"pointer_only_for_licence":4,"official":{"repos":["bytedance/TWIST"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":6,"ran_from_kinds":["official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/self-supervised-learning-by-estimating-twin-1#ran","syntology_url":"https://syntology.ai/paper/2110.07402","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2110.07402"}}}},{"paper":"/paper/with-a-little-help-from-my-friends-nearest","slug":"with-a-little-help-from-my-friends-nearest","title":"With a Little Help from My Friends: Nearest-Neighbor Contrastive Learning of Visual Representations","date":"2021-04-29","arxiv_id":"2104.14548","rows_on_this_dataset":1,"code_links":4,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":5,"samples_ran":4,"samples_constructed":4,"samples_ran_checked":4,"samples_ran_instrument_failed":0,"samples_unverified":1,"pointer_only_for_licence":0,"official":null,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/with-a-little-help-from-my-friends-nearest#ran","syntology_url":"https://syntology.ai/paper/2104.14548","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2104.14548"}}}},{"paper":"/paper/learning-transferable-visual-models-from","slug":"learning-transferable-visual-models-from","title":"Learning Transferable Visual Models From Natural Language Supervision","date":"2021-02-26","arxiv_id":"2103.00020","rows_on_this_dataset":1,"code_links":82,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":20,"samples_ran":16,"samples_constructed":0,"samples_ran_checked":2,"samples_ran_instrument_failed":14,"samples_unverified":4,"pointer_only_for_licence":16,"official":{"repos":["openai/CLIP"],"state":"official: no sample here; runs from other or unrecorded repositories","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed","unlocated"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/learning-transferable-visual-models-from#ran","syntology_url":"https://syntology.ai/paper/2103.00020","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2103.00020"}}}},{"paper":"/paper/learning-to-compose-hypercolumns-for-visual","slug":"learning-to-compose-hypercolumns-for-visual","title":"Learning to Compose Hypercolumns for Visual Correspondence","date":"2020-07-21","arxiv_id":"2007.10587","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":5,"samples_ran":3,"samples_constructed":0,"samples_ran_checked":3,"samples_ran_instrument_failed":0,"samples_unverified":2,"pointer_only_for_licence":0,"official":{"repos":["juhongm999/dhpf"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":2,"ran_from_kinds":["official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/learning-to-compose-hypercolumns-for-visual#ran","syntology_url":"https://syntology.ai/paper/2007.10587","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2007.10587"}}}},{"paper":"/paper/hyperpixel-flow-semantic-correspondence-with","slug":"hyperpixel-flow-semantic-correspondence-with","title":"Hyperpixel Flow: Semantic Correspondence with Multi-layer Neural Features","date":"2019-08-18","arxiv_id":"1908.06537","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":14,"samples_ran":12,"samples_constructed":0,"samples_ran_checked":12,"samples_ran_instrument_failed":0,"samples_unverified":2,"pointer_only_for_licence":0,"official":{"repos":["juhongm999/hpf"],"state":"official (archive's flag): 12 ran","n_ran":12,"n_constructed":0,"n_ran_no_instrument_failure":12,"n_unverified":2,"ran_from_kinds":["official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/hyperpixel-flow-semantic-correspondence-with#ran","syntology_url":"https://syntology.ai/paper/1908.06537","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1908.06537"}}}},{"paper":"/paper/block-neural-autoregressive-flow","slug":"block-neural-autoregressive-flow","title":"Block Neural Autoregressive Flow","date":"2019-04-09","arxiv_id":"1904.04676","rows_on_this_dataset":1,"code_links":4,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":4,"samples_ran":2,"samples_constructed":0,"samples_ran_checked":0,"samples_ran_instrument_failed":2,"samples_unverified":2,"pointer_only_for_licence":0,"official":{"repos":["nicola-decao/BNAF"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":2,"ran_from_kinds":["official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/block-neural-autoregressive-flow#ran","syntology_url":"https://syntology.ai/paper/1904.04676","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1904.04676"}}}},{"paper":"/paper/ffjord-free-form-continuous-dynamics-for","slug":"ffjord-free-form-continuous-dynamics-for","title":"FFJORD: Free-form Continuous Dynamics for Scalable Reversible Generative Models","date":"2018-10-02","arxiv_id":"1810.01367","rows_on_this_dataset":1,"code_links":7,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":5,"samples_ran":4,"samples_constructed":2,"samples_ran_checked":4,"samples_ran_instrument_failed":0,"samples_unverified":1,"pointer_only_for_licence":0,"official":null,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/ffjord-free-form-continuous-dynamics-for#ran","syntology_url":"https://syntology.ai/paper/1810.01367","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1810.01367"}}}},{"paper":"/paper/autoaugment-learning-augmentation-policies","slug":"autoaugment-learning-augmentation-policies","title":"AutoAugment: Learning Augmentation Policies from Data","date":"2018-05-24","arxiv_id":"1805.09501","rows_on_this_dataset":1,"code_links":33,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":43,"samples_ran":25,"samples_constructed":0,"samples_ran_checked":22,"samples_ran_instrument_failed":3,"samples_unverified":18,"pointer_only_for_licence":4,"official":{"repos":["tensorflow/models"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/autoaugment-learning-augmentation-policies#ran","syntology_url":"https://syntology.ai/paper/1805.09501","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1805.09501"}}}}],"record_sha256":"b902841c35b778b9c42f194db1a3c5bd750b2e2a454c497c97a19f8a2345baca","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}