{"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/cub-200-2011/papers/3","list_of":"/dataset/cub-200-2011","dataset":"CUB-200-2011","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":"archive","order_definition":"date (newest first), then slug","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":3,"pages_in_order":3,"rows_per_page":100,"rows":[201,211],"of":211,"counts":{"papers_with_a_benchmark_row":211,"with_a_code_link":171,"where_syntology_ran_a_sample":74,"not_listed_spam_title":0,"listed":211,"listed_where_code_ran":74,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":66,"every_run_a_failure_of_syntologys_instrument":8,"listed_with_a_run_with_no_instrument_failure":66,"listed_every_run_a_failure_of_syntologys_instrument":8,"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/cub-200-2011","prev":"/dataset/cub-200-2011/papers/2","next":null,"papers":[{"paper":"/paper/learning-deep-representations-of-fine-grained","slug":"learning-deep-representations-of-fine-grained","title":"Learning Deep Representations of Fine-grained Visual Descriptions","date":"2016-05-17","arxiv_id":"1605.05395","rows_on_this_dataset":3,"code_links":9,"syntology":null},{"paper":"/paper/joint-unsupervised-learning-of-deep","slug":"joint-unsupervised-learning-of-deep","title":"Joint Unsupervised Learning of Deep Representations and Image Clusters","date":"2016-04-13","arxiv_id":"1604.03628","rows_on_this_dataset":1,"code_links":3,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":1,"samples_ran":1,"samples_constructed":0,"samples_ran_checked":1,"samples_ran_instrument_failed":0,"samples_unverified":0,"pointer_only_for_licence":0,"official":{"repos":["jwyang/joint-unsupervised-learning"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/joint-unsupervised-learning-of-deep#ran","syntology_url":"https://syntology.ai/paper/1604.03628","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1604.03628"}}}},{"paper":"/paper/synthesized-classifiers-for-zero-shot","slug":"synthesized-classifiers-for-zero-shot","title":"Synthesized Classifiers for Zero-Shot Learning","date":"2016-03-02","arxiv_id":"1603.00550","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":3,"samples_ran":0,"samples_constructed":0,"samples_ran_checked":0,"samples_ran_instrument_failed":0,"samples_unverified":3,"pointer_only_for_licence":2,"official":null,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/synthesized-classifiers-for-zero-shot#ran","syntology_url":"https://syntology.ai/paper/1603.00550","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1603.00550"}}}},{"paper":"/paper/why-should-i-trust-you-explaining-the","slug":"why-should-i-trust-you-explaining-the","title":"\"Why Should I Trust You?\": Explaining the Predictions of Any Classifier","date":"2016-02-16","arxiv_id":"1602.04938","rows_on_this_dataset":1,"code_links":27,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":19,"samples_ran":13,"samples_constructed":4,"samples_ran_checked":13,"samples_ran_instrument_failed":0,"samples_unverified":6,"pointer_only_for_licence":0,"official":{"repos":["marcotcr/lime-experiments"],"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":["listed","official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/why-should-i-trust-you-explaining-the#ran","syntology_url":"https://syntology.ai/paper/1602.04938","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1602.04938"}}}},{"paper":"/paper/bilinear-cnn-models-for-fine-grained-visual","slug":"bilinear-cnn-models-for-fine-grained-visual","title":"Bilinear CNN Models for Fine-Grained Visual Recognition","date":"2015-12-01","arxiv_id":null,"rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/zero-shot-learning-via-semantic-similarity","slug":"zero-shot-learning-via-semantic-similarity","title":"Zero-Shot Learning via Semantic Similarity Embedding","date":"2015-09-15","arxiv_id":"1509.04767","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/evaluation-of-output-embeddings-for-fine","slug":"evaluation-of-output-embeddings-for-fine","title":"Evaluation of Output Embeddings for Fine-Grained Image Classification","date":"2014-09-30","arxiv_id":"1409.8403","rows_on_this_dataset":3,"code_links":2,"syntology":null},{"paper":"/paper/part-based-r-cnns-for-fine-grained-category","slug":"part-based-r-cnns-for-fine-grained-category","title":"Part-based R-CNNs for Fine-grained Category Detection","date":"2014-07-15","arxiv_id":"1407.3867","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/deep-inside-convolutional-networks","slug":"deep-inside-convolutional-networks","title":"Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps","date":"2013-12-20","arxiv_id":"1312.6034","rows_on_this_dataset":1,"code_links":23,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":4,"samples_ran":1,"samples_constructed":0,"samples_ran_checked":0,"samples_ran_instrument_failed":1,"samples_unverified":3,"pointer_only_for_licence":1,"official":null,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/deep-inside-convolutional-networks#ran","syntology_url":"https://syntology.ai/paper/1312.6034","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1312.6034"}}}},{"paper":"/paper/deformable-part-descriptors-for-fine-grained","slug":"deformable-part-descriptors-for-fine-grained","title":"Deformable Part Descriptors for Fine-grained Recognition and Attribute Prediction","date":"2013-12-01","arxiv_id":null,"rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/label-embedding-for-attribute-based","slug":"label-embedding-for-attribute-based","title":"Label-Embedding for Attribute-Based Classification","date":"2013-06-01","arxiv_id":null,"rows_on_this_dataset":1,"code_links":0,"syntology":null}],"record_sha256":"986959920862fae1247e548e490056917985ce9fbe27d63f50ff9dfb05e9c51c","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}