{"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/imagenet-s/papers/ran/1","list_of":"/dataset/imagenet-s","dataset":"ImageNet-S","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":[1,16],"of":16,"counts":{"papers_with_a_benchmark_row":19,"with_a_code_link":19,"where_syntology_ran_a_sample":16,"not_listed_spam_title":0,"listed":19,"listed_where_code_ran":16,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":15,"every_run_a_failure_of_syntologys_instrument":1,"listed_with_a_run_with_no_instrument_failure":15,"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/imagenet-s/papers/ran/1","prev":null,"next":null,"papers":[{"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/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/prompt-pre-training-with-twenty-thousand-1","slug":"prompt-pre-training-with-twenty-thousand-1","title":"Prompt Pre-Training with Twenty-Thousand Classes for Open-Vocabulary Visual Recognition","date":"2023-04-10","arxiv_id":"2304.04704","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":7,"samples_ran":5,"samples_constructed":0,"samples_ran_checked":2,"samples_ran_instrument_failed":3,"samples_unverified":2,"pointer_only_for_licence":3,"official":{"repos":["amazon-science/prompt-pretraining"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":2,"ran_from_kinds":["official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/prompt-pre-training-with-twenty-thousand-1#ran","syntology_url":"https://syntology.ai/paper/2304.04704","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2304.04704"}}}},{"paper":"/paper/towards-sustainable-self-supervised-learning","slug":"towards-sustainable-self-supervised-learning","title":"Towards Sustainable Self-supervised Learning","date":"2022-10-20","arxiv_id":"2210.11016","rows_on_this_dataset":4,"code_links":1,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":8,"samples_ran":6,"samples_constructed":6,"samples_ran_checked":6,"samples_ran_instrument_failed":0,"samples_unverified":2,"pointer_only_for_licence":8,"official":{"repos":["sail-sg/tec"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":6,"n_ran_no_instrument_failure":6,"n_unverified":2,"ran_from_kinds":["official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/towards-sustainable-self-supervised-learning#ran","syntology_url":"https://syntology.ai/paper/2210.11016","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.11016"}}}},{"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/pali-a-jointly-scaled-multilingual-language","slug":"pali-a-jointly-scaled-multilingual-language","title":"PaLI: A Jointly-Scaled Multilingual Language-Image Model","date":"2022-09-14","arxiv_id":"2209.06794","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":4,"samples_ran":2,"samples_constructed":1,"samples_ran_checked":2,"samples_ran_instrument_failed":0,"samples_unverified":2,"pointer_only_for_licence":0,"official":{"repos":["google-research/big_vision"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":1,"n_ran_no_instrument_failure":2,"n_unverified":2,"ran_from_kinds":["official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/pali-a-jointly-scaled-multilingual-language#ran","syntology_url":"https://syntology.ai/paper/2209.06794","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2209.06794"}}}},{"paper":"/paper/rf-next-efficient-receptive-field-search-for","slug":"rf-next-efficient-receptive-field-search-for","title":"RF-Next: Efficient Receptive Field Search for Convolutional Neural Networks","date":"2022-06-14","arxiv_id":"2206.06637","rows_on_this_dataset":3,"code_links":2,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":3,"samples_ran":2,"samples_constructed":0,"samples_ran_checked":1,"samples_ran_instrument_failed":1,"samples_unverified":1,"pointer_only_for_licence":3,"official":{"repos":["ShangHua-Gao/RFNext"],"state":"official (archive's flag): 2 ran","n_ran":2,"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/rf-next-efficient-receptive-field-search-for#ran","syntology_url":"https://syntology.ai/paper/2206.06637","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2206.06637"}}}},{"paper":"/paper/exploring-feature-self-relation-for-self","slug":"exploring-feature-self-relation-for-self","title":"SERE: Exploring Feature Self-relation for Self-supervised Transformer","date":"2022-06-10","arxiv_id":"2206.05184","rows_on_this_dataset":6,"code_links":1,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":3,"samples_ran":1,"samples_constructed":0,"samples_ran_checked":0,"samples_ran_instrument_failed":1,"samples_unverified":2,"pointer_only_for_licence":3,"official":{"repos":["MCG-NKU/SERE"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":2,"ran_from_kinds":["unlocated"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/exploring-feature-self-relation-for-self#ran","syntology_url":"https://syntology.ai/paper/2206.05184","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2206.05184"}}}},{"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/a-convnet-for-the-2020s","slug":"a-convnet-for-the-2020s","title":"A ConvNet for the 2020s","date":"2022-01-10","arxiv_id":"2201.03545","rows_on_this_dataset":1,"code_links":54,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":80,"samples_ran":57,"samples_constructed":39,"samples_ran_checked":49,"samples_ran_instrument_failed":8,"samples_unverified":23,"pointer_only_for_licence":12,"official":{"repos":["facebookresearch/ConvNeXt"],"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/a-convnet-for-the-2020s#ran","syntology_url":"https://syntology.ai/paper/2201.03545","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2201.03545"}}}},{"paper":"/paper/masked-autoencoders-are-scalable-vision","slug":"masked-autoencoders-are-scalable-vision","title":"Masked Autoencoders Are Scalable Vision Learners","date":"2021-11-11","arxiv_id":"2111.06377","rows_on_this_dataset":4,"code_links":58,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":137,"samples_ran":86,"samples_constructed":40,"samples_ran_checked":69,"samples_ran_instrument_failed":17,"samples_unverified":51,"pointer_only_for_licence":78,"official":{"repos":["facebookresearch/mae"],"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/masked-autoencoders-are-scalable-vision#ran","syntology_url":"https://syntology.ai/paper/2111.06377","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2111.06377"}}}},{"paper":"/paper/picie-unsupervised-semantic-segmentation","slug":"picie-unsupervised-semantic-segmentation","title":"PiCIE: Unsupervised Semantic Segmentation using Invariance and Equivariance in Clustering","date":"2021-03-30","arxiv_id":"2103.17070","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":4,"samples_ran":2,"samples_constructed":2,"samples_ran_checked":2,"samples_ran_instrument_failed":0,"samples_unverified":2,"pointer_only_for_licence":1,"official":{"repos":["janghyuncho/PiCIE"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":1,"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/picie-unsupervised-semantic-segmentation#ran","syntology_url":"https://syntology.ai/paper/2103.17070","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2103.17070"}}}},{"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/unsupervised-semantic-segmentation-by","slug":"unsupervised-semantic-segmentation-by","title":"Unsupervised Semantic Segmentation by Contrasting Object Mask Proposals","date":"2021-02-11","arxiv_id":"2102.06191","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":2,"samples_ran":2,"samples_constructed":2,"samples_ran_checked":2,"samples_ran_instrument_failed":0,"samples_unverified":0,"pointer_only_for_licence":2,"official":{"repos":["wvangansbeke/Unsupervised-Semantic-Segmentation"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["listed","official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/unsupervised-semantic-segmentation-by#ran","syntology_url":"https://syntology.ai/paper/2102.06191","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2102.06191"}}}},{"paper":"/paper/deep-clustering-for-unsupervised-learning-of","slug":"deep-clustering-for-unsupervised-learning-of","title":"Deep Clustering for Unsupervised Learning of Visual Features","date":"2018-07-15","arxiv_id":"1807.05520","rows_on_this_dataset":1,"code_links":9,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":7,"samples_ran":5,"samples_constructed":2,"samples_ran_checked":3,"samples_ran_instrument_failed":2,"samples_unverified":2,"pointer_only_for_licence":4,"official":{"repos":["facebookresearch/deepcluster"],"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/deep-clustering-for-unsupervised-learning-of#ran","syntology_url":"https://syntology.ai/paper/1807.05520","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1807.05520"}}}}],"record_sha256":"c4689131d888b2966118d458eb01c8e259fba44739cc06edd8df41b2e4b2bc30","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}