{"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/ran/1","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":"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":[1,74],"of":74,"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/papers/ran/1","prev":null,"next":null,"papers":[{"paper":"/paper/enhancing-cognition-and-explainability-of","slug":"enhancing-cognition-and-explainability-of","title":"Enhancing Cognition and Explainability of Multimodal Foundation Models with Self-Synthesized Data","date":"2025-02-19","arxiv_id":"2502.14044","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":2,"samples_ran":2,"samples_constructed":0,"samples_ran_checked":1,"samples_ran_instrument_failed":1,"samples_unverified":0,"pointer_only_for_licence":1,"official":{"repos":["sycny/selfsynthx"],"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","unlocated"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/enhancing-cognition-and-explainability-of#ran","syntology_url":"https://syntology.ai/paper/2502.14044","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2502.14044"}}}},{"paper":"/paper/few-shot-tuning-of-foundation-models-for","slug":"few-shot-tuning-of-foundation-models-for","title":"Few-shot Tuning of Foundation Models for Class-incremental Learning","date":"2024-05-26","arxiv_id":"2405.16625","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":10,"samples_ran":6,"samples_constructed":0,"samples_ran_checked":6,"samples_ran_instrument_failed":0,"samples_unverified":4,"pointer_only_for_licence":0,"official":{"repos":["shuvenduroy/coact-fscil"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":4,"ran_from_kinds":["official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/few-shot-tuning-of-foundation-models-for#ran","syntology_url":"https://syntology.ai/paper/2405.16625","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2405.16625"}}}},{"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/pre-trained-vision-and-language-transformers","slug":"pre-trained-vision-and-language-transformers","title":"Pre-trained Vision and Language Transformers Are Few-Shot Incremental Learners","date":"2024-04-02","arxiv_id":"2404.02117","rows_on_this_dataset":2,"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":8,"samples_ran_instrument_failed":4,"samples_unverified":2,"pointer_only_for_licence":1,"official":{"repos":["khu-agi/privilege"],"state":"official (archive's flag): 12 ran","n_ran":12,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":2,"ran_from_kinds":["official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/pre-trained-vision-and-language-transformers#ran","syntology_url":"https://syntology.ai/paper/2404.02117","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2404.02117"}}}},{"paper":"/paper/less-is-more-fewer-interpretable-region-via","slug":"less-is-more-fewer-interpretable-region-via","title":"Less is More: Fewer Interpretable Region via Submodular Subset Selection","date":"2024-02-14","arxiv_id":"2402.09164","rows_on_this_dataset":2,"code_links":1,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":3,"samples_ran":3,"samples_constructed":2,"samples_ran_checked":2,"samples_ran_instrument_failed":1,"samples_unverified":0,"pointer_only_for_licence":3,"official":{"repos":["ruoyuchen10/smdl-attribution"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":2,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/less-is-more-fewer-interpretable-region-via#ran","syntology_url":"https://syntology.ai/paper/2402.09164","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.09164"}}}},{"paper":"/paper/learning-semantic-proxies-from-visual-prompts","slug":"learning-semantic-proxies-from-visual-prompts","title":"Learning Semantic Proxies from Visual Prompts for Parameter-Efficient Fine-Tuning in Deep Metric Learning","date":"2024-02-04","arxiv_id":"2402.02340","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":3,"samples_ran_checked":3,"samples_ran_instrument_failed":0,"samples_unverified":2,"pointer_only_for_licence":0,"official":{"repos":["noahsark/parameterefficient-dml"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":3,"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-semantic-proxies-from-visual-prompts#ran","syntology_url":"https://syntology.ai/paper/2402.02340","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.02340"}}}},{"paper":"/paper/context-aware-meta-learning","slug":"context-aware-meta-learning","title":"Context-Aware Meta-Learning","date":"2023-10-17","arxiv_id":"2310.10971","rows_on_this_dataset":2,"code_links":1,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":16,"samples_ran":5,"samples_constructed":4,"samples_ran_checked":5,"samples_ran_instrument_failed":0,"samples_unverified":11,"pointer_only_for_licence":0,"official":{"repos":["cfifty/CAML"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":4,"n_ran_no_instrument_failure":5,"n_unverified":11,"ran_from_kinds":["official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/context-aware-meta-learning#ran","syntology_url":"https://syntology.ai/paper/2310.10971","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2310.10971"}}}},{"paper":"/paper/generative-prompt-model-for-weakly-supervised","slug":"generative-prompt-model-for-weakly-supervised","title":"Generative Prompt Model for Weakly Supervised Object Localization","date":"2023-07-19","arxiv_id":"2307.09756","rows_on_this_dataset":2,"code_links":1,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":1,"samples_ran":1,"samples_constructed":1,"samples_ran_checked":1,"samples_ran_instrument_failed":0,"samples_unverified":0,"pointer_only_for_licence":0,"official":{"repos":["callsys/genpromp"],"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":["official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/generative-prompt-model-for-weakly-supervised#ran","syntology_url":"https://syntology.ai/paper/2307.09756","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2307.09756"}}}},{"paper":"/paper/unsupervised-semantic-correspondence-using","slug":"unsupervised-semantic-correspondence-using","title":"Unsupervised Semantic Correspondence Using Stable Diffusion","date":"2023-05-24","arxiv_id":"2305.15581","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":12,"samples_ran":11,"samples_constructed":0,"samples_ran_checked":9,"samples_ran_instrument_failed":2,"samples_unverified":1,"pointer_only_for_licence":2,"official":{"repos":["ubc-vision/LDM_correspondences"],"state":"official (archive's flag): 11 ran","n_ran":11,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":1,"ran_from_kinds":["official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/unsupervised-semantic-correspondence-using#ran","syntology_url":"https://syntology.ai/paper/2305.15581","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.15581"}}}},{"paper":"/paper/unicom-universal-and-compact-representation","slug":"unicom-universal-and-compact-representation","title":"Unicom: Universal and Compact Representation Learning for Image Retrieval","date":"2023-04-12","arxiv_id":"2304.05884","rows_on_this_dataset":1,"code_links":3,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":6,"samples_ran":3,"samples_constructed":0,"samples_ran_checked":1,"samples_ran_instrument_failed":2,"samples_unverified":3,"pointer_only_for_licence":6,"official":{"repos":["deepglint/unicom"],"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":["unlocated"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/unicom-universal-and-compact-representation#ran","syntology_url":"https://syntology.ai/paper/2304.05884","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2304.05884"}}}},{"paper":"/paper/galip-generative-adversarial-clips-for-text","slug":"galip-generative-adversarial-clips-for-text","title":"GALIP: Generative Adversarial CLIPs for Text-to-Image Synthesis","date":"2023-01-30","arxiv_id":"2301.12959","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":11,"samples_ran":7,"samples_constructed":0,"samples_ran_checked":6,"samples_ran_instrument_failed":1,"samples_unverified":4,"pointer_only_for_licence":2,"official":{"repos":["tobran/galip"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":3,"ran_from_kinds":["listed","official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/galip-generative-adversarial-clips-for-text#ran","syntology_url":"https://syntology.ai/paper/2301.12959","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2301.12959"}}}},{"paper":"/paper/weakly-supervised-object-localization-via","slug":"weakly-supervised-object-localization-via","title":"Weakly Supervised Object Localization via Transformer with Implicit Spatial Calibration","date":"2022-07-21","arxiv_id":"2207.10447","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":10,"samples_ran":7,"samples_constructed":3,"samples_ran_checked":4,"samples_ran_instrument_failed":3,"samples_unverified":3,"pointer_only_for_licence":0,"official":{"repos":["164140757/scm"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":3,"n_ran_no_instrument_failure":4,"n_unverified":3,"ran_from_kinds":["official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/weakly-supervised-object-localization-via#ran","syntology_url":"https://syntology.ai/paper/2207.10447","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2207.10447"}}}},{"paper":"/paper/task-discrepancy-maximization-for-fine-1","slug":"task-discrepancy-maximization-for-fine-1","title":"Task Discrepancy Maximization for Fine-grained Few-Shot Classification","date":"2022-07-04","arxiv_id":"2207.01376","rows_on_this_dataset":2,"code_links":1,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":2,"samples_ran":2,"samples_constructed":0,"samples_ran_checked":2,"samples_ran_instrument_failed":0,"samples_unverified":0,"pointer_only_for_licence":2,"official":{"repos":["leesb7426/cvpr2022-task-discrepancy-maximization-for-fine-grained-few-shot-classification"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/task-discrepancy-maximization-for-fine-1#ran","syntology_url":"https://syntology.ai/paper/2207.01376","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2207.01376"}}}},{"paper":"/paper/making-sense-of-dependence-efficient-black","slug":"making-sense-of-dependence-efficient-black","title":"Making Sense of Dependence: Efficient Black-box Explanations Using Dependence Measure","date":"2022-06-13","arxiv_id":"2206.06219","rows_on_this_dataset":2,"code_links":1,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":1,"samples_ran":1,"samples_constructed":0,"samples_ran_checked":0,"samples_ran_instrument_failed":1,"samples_unverified":0,"pointer_only_for_licence":1,"official":{"repos":["paulnovello/hsic-attribution-method"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/making-sense-of-dependence-efficient-black#ran","syntology_url":"https://syntology.ai/paper/2206.06219","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2206.06219"}}}},{"paper":"/paper/attributable-visual-similarity-learning","slug":"attributable-visual-similarity-learning","title":"Attributable Visual Similarity Learning","date":"2022-03-28","arxiv_id":"2203.14932","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":3,"samples_ran_instrument_failed":0,"samples_unverified":1,"pointer_only_for_licence":0,"official":{"repos":["zbr17/avsl"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":1,"ran_from_kinds":["official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/attributable-visual-similarity-learning#ran","syntology_url":"https://syntology.ai/paper/2203.14932","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.14932"}}}},{"paper":"/paper/hyperbolic-vision-transformers-combining","slug":"hyperbolic-vision-transformers-combining","title":"Hyperbolic Vision Transformers: Combining Improvements in Metric Learning","date":"2022-03-21","arxiv_id":"2203.10833","rows_on_this_dataset":2,"code_links":2,"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":0,"official":{"repos":["htdt/hyp_metric"],"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/hyperbolic-vision-transformers-combining#ran","syntology_url":"https://syntology.ai/paper/2203.10833","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.10833"}}}},{"paper":"/paper/integrating-language-guidance-into-vision","slug":"integrating-language-guidance-into-vision","title":"Integrating Language Guidance into Vision-based Deep Metric Learning","date":"2022-03-16","arxiv_id":"2203.08543","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":5,"samples_ran":4,"samples_constructed":0,"samples_ran_checked":4,"samples_ran_instrument_failed":0,"samples_unverified":1,"pointer_only_for_licence":0,"official":{"repos":["explainableml/languageguidance_for_dml"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":1,"ran_from_kinds":["official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/integrating-language-guidance-into-vision#ran","syntology_url":"https://syntology.ai/paper/2203.08543","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.08543"}}}},{"paper":"/paper/non-isotropy-regularization-for-proxy-based","slug":"non-isotropy-regularization-for-proxy-based","title":"Non-isotropy Regularization for Proxy-based Deep Metric Learning","date":"2022-03-16","arxiv_id":"2203.08547","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":3,"samples_ran":2,"samples_constructed":0,"samples_ran_checked":2,"samples_ran_instrument_failed":0,"samples_unverified":1,"pointer_only_for_licence":0,"official":{"repos":["explainableml/nonisotropicproxydml"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":1,"ran_from_kinds":["official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/non-isotropy-regularization-for-proxy-based#ran","syntology_url":"https://syntology.ai/paper/2203.08547","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.08547"}}}},{"paper":"/paper/self-supervised-transformers-for-unsupervised","slug":"self-supervised-transformers-for-unsupervised","title":"Self-Supervised Transformers for Unsupervised Object Discovery using Normalized Cut","date":"2022-02-23","arxiv_id":"2202.11539","rows_on_this_dataset":2,"code_links":1,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":1,"samples_ran":1,"samples_constructed":0,"samples_ran_checked":0,"samples_ran_instrument_failed":1,"samples_unverified":0,"pointer_only_for_licence":0,"official":null,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/self-supervised-transformers-for-unsupervised#ran","syntology_url":"https://syntology.ai/paper/2202.11539","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2202.11539"}}}},{"paper":"/paper/truncated-diffusion-probabilistic-models","slug":"truncated-diffusion-probabilistic-models","title":"Truncated Diffusion Probabilistic Models and Diffusion-based Adversarial Auto-Encoders","date":"2022-02-19","arxiv_id":"2202.09671","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":3,"samples_ran":3,"samples_constructed":0,"samples_ran_checked":1,"samples_ran_instrument_failed":2,"samples_unverified":0,"pointer_only_for_licence":0,"official":{"repos":["jegzheng/truncated-diffusion-probabilistic-models"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["community"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/truncated-diffusion-probabilistic-models#ran","syntology_url":"https://syntology.ai/paper/2202.09671","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2202.09671"}}}},{"paper":"/paper/lafite-towards-language-free-training-for","slug":"lafite-towards-language-free-training-for","title":"LAFITE: Towards Language-Free Training for Text-to-Image Generation","date":"2021-11-27","arxiv_id":"2111.13792","rows_on_this_dataset":1,"code_links":3,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":18,"samples_ran":12,"samples_constructed":0,"samples_ran_checked":10,"samples_ran_instrument_failed":2,"samples_unverified":6,"pointer_only_for_licence":3,"official":{"repos":["drboog/Lafite"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":3,"ran_from_kinds":["listed","official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/lafite-towards-language-free-training-for#ran","syntology_url":"https://syntology.ai/paper/2111.13792","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2111.13792"}}}},{"paper":"/paper/projected-gans-converge-faster","slug":"projected-gans-converge-faster","title":"Projected GANs Converge Faster","date":"2021-11-01","arxiv_id":"2111.01007","rows_on_this_dataset":1,"code_links":3,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":49,"samples_ran":38,"samples_constructed":14,"samples_ran_checked":20,"samples_ran_instrument_failed":18,"samples_unverified":11,"pointer_only_for_licence":6,"official":{"repos":["autonomousvision/projected_gan"],"state":"official (archive's flag): 12 ran","n_ran":12,"n_constructed":5,"n_ran_no_instrument_failure":6,"n_unverified":5,"ran_from_kinds":["listed","official","unlocated"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/projected-gans-converge-faster#ran","syntology_url":"https://syntology.ai/paper/2111.01007","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2111.01007"}}}},{"paper":"/paper/robust-and-decomposable-average-precision-for","slug":"robust-and-decomposable-average-precision-for","title":"Robust and Decomposable Average Precision for Image Retrieval","date":"2021-10-01","arxiv_id":"2110.01445","rows_on_this_dataset":2,"code_links":1,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":5,"samples_ran":5,"samples_constructed":0,"samples_ran_checked":2,"samples_ran_instrument_failed":3,"samples_unverified":0,"pointer_only_for_licence":2,"official":{"repos":["elias-ramzi/roadmap"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/robust-and-decomposable-average-precision-for#ran","syntology_url":"https://syntology.ai/paper/2110.01445","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2110.01445"}}}},{"paper":"/paper/relational-embedding-for-few-shot","slug":"relational-embedding-for-few-shot","title":"Relational Embedding for Few-Shot Classification","date":"2021-08-22","arxiv_id":"2108.09666","rows_on_this_dataset":2,"code_links":1,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":13,"samples_ran":11,"samples_constructed":10,"samples_ran_checked":11,"samples_ran_instrument_failed":0,"samples_unverified":2,"pointer_only_for_licence":0,"official":{"repos":["dahyun-kang/renet"],"state":"official (archive's flag): 11 ran","n_ran":11,"n_constructed":10,"n_ran_no_instrument_failure":11,"n_unverified":2,"ran_from_kinds":["official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/relational-embedding-for-few-shot#ran","syntology_url":"https://syntology.ai/paper/2108.09666","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2108.09666"}}}},{"paper":"/paper/counterfactual-attention-learning-for-fine","slug":"counterfactual-attention-learning-for-fine","title":"Counterfactual Attention Learning for Fine-Grained Visual Categorization and Re-identification","date":"2021-08-19","arxiv_id":"2108.08728","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":11,"samples_ran":8,"samples_constructed":7,"samples_ran_checked":8,"samples_ran_instrument_failed":0,"samples_unverified":3,"pointer_only_for_licence":0,"official":{"repos":["raoyongming/CAL"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":7,"n_ran_no_instrument_failure":8,"n_unverified":3,"ran_from_kinds":["official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/counterfactual-attention-learning-for-fine#ran","syntology_url":"https://syntology.ai/paper/2108.08728","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2108.08728"}}}},{"paper":"/paper/towards-interpretable-deep-metric-learning","slug":"towards-interpretable-deep-metric-learning","title":"Towards Interpretable Deep Metric Learning with Structural Matching","date":"2021-08-12","arxiv_id":"2108.05889","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":2,"samples_ran":2,"samples_constructed":0,"samples_ran_checked":0,"samples_ran_instrument_failed":2,"samples_unverified":0,"pointer_only_for_licence":2,"official":{"repos":["wl-zhao/diml"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/towards-interpretable-deep-metric-learning#ran","syntology_url":"https://syntology.ai/paper/2108.05889","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2108.05889"}}}},{"paper":"/paper/unsupervised-embedding-adaptation-via-early","slug":"unsupervised-embedding-adaptation-via-early","title":"Unsupervised Embedding Adaptation via Early-Stage Feature Reconstruction for Few-Shot Classification","date":"2021-06-22","arxiv_id":"2106.11486","rows_on_this_dataset":2,"code_links":1,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":1,"samples_ran":1,"samples_constructed":1,"samples_ran_checked":1,"samples_ran_instrument_failed":0,"samples_unverified":0,"pointer_only_for_licence":1,"official":{"repos":["movinghoon/ESFR"],"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":["official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/unsupervised-embedding-adaptation-via-early#ran","syntology_url":"https://syntology.ai/paper/2106.11486","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2106.11486"}}}},{"paper":"/paper/entropy-based-logic-explanations-of-neural","slug":"entropy-based-logic-explanations-of-neural","title":"Entropy-based Logic Explanations of Neural Networks","date":"2021-06-12","arxiv_id":"2106.06804","rows_on_this_dataset":4,"code_links":3,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":6,"samples_ran":6,"samples_constructed":0,"samples_ran_checked":6,"samples_ran_instrument_failed":0,"samples_unverified":0,"pointer_only_for_licence":1,"official":{"repos":["pietrobarbiero/entropy-lens","pietrobarbiero/logic_explainer_networks","pietrobarbiero/pytorch_explain"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":0,"ran_from_kinds":["official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/entropy-based-logic-explanations-of-neural#ran","syntology_url":"https://syntology.ai/paper/2106.06804","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2106.06804"}}}},{"paper":"/paper/it-takes-two-to-tango-mixup-for-deep-metric","slug":"it-takes-two-to-tango-mixup-for-deep-metric","title":"It Takes Two to Tango: Mixup for Deep Metric Learning","date":"2021-06-09","arxiv_id":"2106.04990","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":2,"samples_ran":2,"samples_constructed":0,"samples_ran_checked":1,"samples_ran_instrument_failed":1,"samples_unverified":0,"pointer_only_for_licence":0,"official":{"repos":["billpsomas/Metrix_ICLR22"],"state":"official (archive's flag): 2 ran","n_ran":2,"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/it-takes-two-to-tango-mixup-for-deep-metric#ran","syntology_url":"https://syntology.ai/paper/2106.04990","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2106.04990"}}}},{"paper":"/paper/transfg-a-transformer-architecture-for-fine","slug":"transfg-a-transformer-architecture-for-fine","title":"TransFG: A Transformer Architecture for Fine-grained Recognition","date":"2021-03-14","arxiv_id":"2103.07976","rows_on_this_dataset":2,"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":1,"samples_ran_instrument_failed":2,"samples_unverified":0,"pointer_only_for_licence":2,"official":{"repos":["TACJu/TransFG"],"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","unlocated"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/transfg-a-transformer-architecture-for-fine#ran","syntology_url":"https://syntology.ai/paper/2103.07976","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2103.07976"}}}},{"paper":"/paper/s2sd-simultaneous-similarity-based-self","slug":"s2sd-simultaneous-similarity-based-self","title":"S2SD: Simultaneous Similarity-based Self-Distillation for Deep Metric Learning","date":"2020-09-17","arxiv_id":"2009.08348","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":3,"samples_ran":2,"samples_constructed":0,"samples_ran_checked":2,"samples_ran_instrument_failed":0,"samples_unverified":1,"pointer_only_for_licence":0,"official":{"repos":["MLforHealth/S2SD"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":1,"ran_from_kinds":["official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/s2sd-simultaneous-similarity-based-self#ran","syntology_url":"https://syntology.ai/paper/2009.08348","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2009.08348"}}}},{"paper":"/paper/transductive-information-maximization-for-few","slug":"transductive-information-maximization-for-few","title":"Transductive Information Maximization For Few-Shot Learning","date":"2020-08-25","arxiv_id":"2008.11297","rows_on_this_dataset":2,"code_links":2,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":10,"samples_ran":9,"samples_constructed":2,"samples_ran_checked":7,"samples_ran_instrument_failed":2,"samples_unverified":1,"pointer_only_for_licence":1,"official":{"repos":["mboudiaf/TIM"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":1,"ran_from_kinds":["listed","official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/transductive-information-maximization-for-few#ran","syntology_url":"https://syntology.ai/paper/2008.11297","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2008.11297"}}}},{"paper":"/paper/leveraging-the-feature-distribution-in","slug":"leveraging-the-feature-distribution-in","title":"Leveraging the Feature Distribution in Transfer-based Few-Shot Learning","date":"2020-06-06","arxiv_id":"2006.03806","rows_on_this_dataset":2,"code_links":6,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":26,"samples_ran":22,"samples_constructed":0,"samples_ran_checked":13,"samples_ran_instrument_failed":9,"samples_unverified":4,"pointer_only_for_licence":12,"official":{"repos":["yhu01/PT-MAP"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed","official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/leveraging-the-feature-distribution-in#ran","syntology_url":"https://syntology.ai/paper/2006.03806","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2006.03806"}}}},{"paper":"/paper/instance-aware-image-colorization","slug":"instance-aware-image-colorization","title":"Instance-aware Image Colorization","date":"2020-05-21","arxiv_id":"2005.10825","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":7,"samples_ran":6,"samples_constructed":0,"samples_ran_checked":6,"samples_ran_instrument_failed":0,"samples_unverified":1,"pointer_only_for_licence":1,"official":{"repos":["ericsujw/InstColorization"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":1,"ran_from_kinds":["official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/instance-aware-image-colorization#ran","syntology_url":"https://syntology.ai/paper/2005.10825","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2005.10825"}}}},{"paper":"/paper/diva-diverse-visual-feature-aggregation","slug":"diva-diverse-visual-feature-aggregation","title":"DiVA: Diverse Visual Feature Aggregation for Deep Metric Learning","date":"2020-04-28","arxiv_id":"2004.13458","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":5,"samples_ran":3,"samples_constructed":0,"samples_ran_checked":1,"samples_ran_instrument_failed":2,"samples_unverified":2,"pointer_only_for_licence":3,"official":{"repos":["Confusezius/ECCV2020_DiVA_MultiFeature_DML"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["listed","official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/diva-diverse-visual-feature-aggregation#ran","syntology_url":"https://syntology.ai/paper/2004.13458","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2004.13458"}}}},{"paper":"/paper/proxynca-revisiting-and-revitalizing-proxy","slug":"proxynca-revisiting-and-revitalizing-proxy","title":"ProxyNCA++: Revisiting and Revitalizing Proxy Neighborhood Component Analysis","date":"2020-04-02","arxiv_id":"2004.01113","rows_on_this_dataset":2,"code_links":1,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":5,"samples_ran":4,"samples_constructed":0,"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/proxynca-revisiting-and-revitalizing-proxy#ran","syntology_url":"https://syntology.ai/paper/2004.01113","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2004.01113"}}}},{"paper":"/paper/learning-from-small-data-through-sampling-an","slug":"learning-from-small-data-through-sampling-an","title":"Generative Latent Implicit Conditional Optimization when Learning from Small Sample","date":"2020-03-31","arxiv_id":"2003.14297","rows_on_this_dataset":2,"code_links":1,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":16,"samples_ran":10,"samples_constructed":0,"samples_ran_checked":10,"samples_ran_instrument_failed":0,"samples_unverified":6,"pointer_only_for_licence":0,"official":{"repos":["IdanAzuri/glico-learning-small-sample"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":0,"n_ran_no_instrument_failure":10,"n_unverified":6,"ran_from_kinds":["official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/learning-from-small-data-through-sampling-an#ran","syntology_url":"https://syntology.ai/paper/2003.14297","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2003.14297"}}}},{"paper":"/paper/pads-policy-adapted-sampling-for-visual","slug":"pads-policy-adapted-sampling-for-visual","title":"PADS: Policy-Adapted Sampling for Visual Similarity Learning","date":"2020-03-24","arxiv_id":"2003.11113","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":10,"samples_ran":7,"samples_constructed":0,"samples_ran_checked":7,"samples_ran_instrument_failed":0,"samples_unverified":3,"pointer_only_for_licence":0,"official":{"repos":["Confusezius/CVPR2020_PADS"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":3,"ran_from_kinds":["official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/pads-policy-adapted-sampling-for-visual#ran","syntology_url":"https://syntology.ai/paper/2003.11113","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2003.11113"}}}},{"paper":"/paper/metric-learning-cross-entropy-vs-pairwise","slug":"metric-learning-cross-entropy-vs-pairwise","title":"A unifying mutual information view of metric learning: cross-entropy vs. pairwise losses","date":"2020-03-19","arxiv_id":"2003.08983","rows_on_this_dataset":1,"code_links":1,"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":["jeromerony/dml_cross_entropy"],"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/metric-learning-cross-entropy-vs-pairwise#ran","syntology_url":"https://syntology.ai/paper/2003.08983","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2003.08983"}}}},{"paper":"/paper/latent-embedding-feedback-and-discriminative","slug":"latent-embedding-feedback-and-discriminative","title":"Latent Embedding Feedback and Discriminative Features for Zero-Shot Classification","date":"2020-03-17","arxiv_id":"2003.07833","rows_on_this_dataset":2,"code_links":1,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":4,"samples_ran":4,"samples_constructed":0,"samples_ran_checked":4,"samples_ran_instrument_failed":0,"samples_unverified":0,"pointer_only_for_licence":1,"official":{"repos":["akshitac8/tfvaegan"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":0,"ran_from_kinds":["official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/latent-embedding-feedback-and-discriminative#ran","syntology_url":"https://syntology.ai/paper/2003.07833","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2003.07833"}}}},{"paper":"/paper/cross-domain-few-shot-classification-via-1","slug":"cross-domain-few-shot-classification-via-1","title":"Cross-Domain Few-Shot Classification via Learned Feature-Wise Transformation","date":"2020-01-23","arxiv_id":"2001.08735","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":2,"samples_ran":2,"samples_constructed":0,"samples_ran_checked":1,"samples_ran_instrument_failed":1,"samples_unverified":0,"pointer_only_for_licence":2,"official":{"repos":["hytseng0509/CrossDomainFewShot"],"state":"official (archive's flag): 2 ran","n_ran":2,"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/cross-domain-few-shot-classification-via-1#ran","syntology_url":"https://syntology.ai/paper/2001.08735","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2001.08735"}}}},{"paper":"/paper/a-new-benchmark-for-evaluation-of-cross","slug":"a-new-benchmark-for-evaluation-of-cross","title":"A Broader Study of Cross-Domain Few-Shot Learning","date":"2019-12-16","arxiv_id":"1912.07200","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":7,"samples_ran":6,"samples_constructed":0,"samples_ran_checked":5,"samples_ran_instrument_failed":1,"samples_unverified":1,"pointer_only_for_licence":3,"official":null,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/a-new-benchmark-for-evaluation-of-cross#ran","syntology_url":"https://syntology.ai/paper/1912.07200","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1912.07200"}}}},{"paper":"/paper/the-group-loss-for-deep-metric-learning","slug":"the-group-loss-for-deep-metric-learning","title":"The Group Loss for Deep Metric Learning","date":"2019-12-01","arxiv_id":"1912.00385","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":18,"samples_ran":12,"samples_constructed":0,"samples_ran_checked":10,"samples_ran_instrument_failed":2,"samples_unverified":6,"pointer_only_for_licence":0,"official":{"repos":["dvl-tum/group_loss"],"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":["listed","official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/the-group-loss-for-deep-metric-learning#ran","syntology_url":"https://syntology.ai/paper/1912.00385","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1912.00385"}}}},{"paper":"/paper/self-supervised-learning-for-few-shot-image","slug":"self-supervised-learning-for-few-shot-image","title":"Self-Supervised Learning For Few-Shot Image Classification","date":"2019-11-14","arxiv_id":"1911.06045","rows_on_this_dataset":2,"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":1,"samples_ran_instrument_failed":2,"samples_unverified":0,"pointer_only_for_licence":2,"official":{"repos":["Alibaba-AAIG/SSL-FEW-SHOT"],"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":["named_in_paper"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/self-supervised-learning-for-few-shot-image#ran","syntology_url":"https://syntology.ai/paper/1911.06045","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1911.06045"}}}},{"paper":"/paper/decoupling-representation-and-classifier-for","slug":"decoupling-representation-and-classifier-for","title":"Decoupling Representation and Classifier for Long-Tailed Recognition","date":"2019-10-21","arxiv_id":"1910.09217","rows_on_this_dataset":1,"code_links":4,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":2,"samples_ran":2,"samples_constructed":0,"samples_ran_checked":2,"samples_ran_instrument_failed":0,"samples_unverified":0,"pointer_only_for_licence":2,"official":{"repos":["facebookresearch/classifier-balancing"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/decoupling-representation-and-classifier-for#ran","syntology_url":"https://syntology.ai/paper/1910.09217","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1910.09217"}}}},{"paper":"/paper/score-camimproved-visual-explanations-via","slug":"score-camimproved-visual-explanations-via","title":"Score-CAM: Score-Weighted Visual Explanations for Convolutional Neural Networks","date":"2019-10-03","arxiv_id":"1910.01279","rows_on_this_dataset":1,"code_links":9,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":13,"samples_ran":9,"samples_constructed":0,"samples_ran_checked":6,"samples_ran_instrument_failed":3,"samples_unverified":4,"pointer_only_for_licence":2,"official":{"repos":["haofanwang/Score-CAM"],"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":["listed","official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/score-camimproved-visual-explanations-via#ran","syntology_url":"https://syntology.ai/paper/1910.01279","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1910.01279"}}}},{"paper":"/paper/controllable-text-to-image-generation","slug":"controllable-text-to-image-generation","title":"Controllable Text-to-Image Generation","date":"2019-09-16","arxiv_id":"1909.07083","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":10,"samples_ran":7,"samples_constructed":0,"samples_ran_checked":7,"samples_ran_instrument_failed":0,"samples_unverified":3,"pointer_only_for_licence":0,"official":{"repos":["mrlibw/ControlGAN"],"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/controllable-text-to-image-generation#ran","syntology_url":"https://syntology.ai/paper/1909.07083","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1909.07083"}}}},{"paper":"/paper/softtriple-loss-deep-metric-learning-without","slug":"softtriple-loss-deep-metric-learning-without","title":"SoftTriple Loss: Deep Metric Learning Without Triplet Sampling","date":"2019-09-11","arxiv_id":"1909.05235","rows_on_this_dataset":1,"code_links":5,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":4,"samples_ran":3,"samples_constructed":0,"samples_ran_checked":2,"samples_ran_instrument_failed":1,"samples_unverified":1,"pointer_only_for_licence":2,"official":{"repos":["idstcv/SoftTriple"],"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/softtriple-loss-deep-metric-learning-without#ran","syntology_url":"https://syntology.ai/paper/1909.05235","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1909.05235"}}}},{"paper":"/paper/attention-based-dropout-layer-for-weakly-1","slug":"attention-based-dropout-layer-for-weakly-1","title":"Attention-based Dropout Layer for Weakly Supervised Object Localization","date":"2019-08-27","arxiv_id":"1908.10028","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":4,"samples_ran_instrument_failed":0,"samples_unverified":2,"pointer_only_for_licence":0,"official":null,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/attention-based-dropout-layer-for-weakly-1#ran","syntology_url":"https://syntology.ai/paper/1908.10028","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1908.10028"}}}},{"paper":"/paper/metric-learning-with-horde-high-order","slug":"metric-learning-with-horde-high-order","title":"Metric Learning With HORDE: High-Order Regularizer for Deep Embeddings","date":"2019-08-07","arxiv_id":"1908.02735","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":4,"samples_ran_instrument_failed":0,"samples_unverified":4,"pointer_only_for_licence":0,"official":{"repos":["pierre-jacob/ICCV2019-Horde"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":4,"ran_from_kinds":["official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/metric-learning-with-horde-high-order#ran","syntology_url":"https://syntology.ai/paper/1908.02735","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1908.02735"}}}},{"paper":"/paper/learning-imbalanced-datasets-with-label","slug":"learning-imbalanced-datasets-with-label","title":"Learning Imbalanced Datasets with Label-Distribution-Aware Margin Loss","date":"2019-06-18","arxiv_id":"1906.07413","rows_on_this_dataset":1,"code_links":7,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":11,"samples_ran":6,"samples_constructed":1,"samples_ran_checked":5,"samples_ran_instrument_failed":1,"samples_unverified":5,"pointer_only_for_licence":5,"official":{"repos":["kaidic/LDAM-DRW"],"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/learning-imbalanced-datasets-with-label#ran","syntology_url":"https://syntology.ai/paper/1906.07413","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1906.07413"}}}},{"paper":"/paper/learning-to-learn-by-self-critique","slug":"learning-to-learn-by-self-critique","title":"Learning to learn via Self-Critique","date":"2019-05-24","arxiv_id":"1905.10295","rows_on_this_dataset":4,"code_links":1,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":13,"samples_ran":13,"samples_constructed":0,"samples_ran_checked":11,"samples_ran_instrument_failed":2,"samples_unverified":0,"pointer_only_for_licence":1,"official":null,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/learning-to-learn-by-self-critique#ran","syntology_url":"https://syntology.ai/paper/1905.10295","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1905.10295"}}}},{"paper":"/paper/tafe-net-task-aware-feature-embeddings-for-1","slug":"tafe-net-task-aware-feature-embeddings-for-1","title":"TAFE-Net: Task-Aware Feature Embeddings for Low Shot Learning","date":"2019-04-11","arxiv_id":"1904.05967","rows_on_this_dataset":1,"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":1,"samples_ran_instrument_failed":0,"samples_unverified":2,"pointer_only_for_licence":0,"official":{"repos":["ucbdrive/tafe-net"],"state":"official (archive's flag): 1 ran","n_ran":1,"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/tafe-net-task-aware-feature-embeddings-for-1#ran","syntology_url":"https://syntology.ai/paper/1904.05967","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1904.05967"}}}},{"paper":"/paper/leveraging-the-invariant-side-of-generative","slug":"leveraging-the-invariant-side-of-generative","title":"Leveraging the Invariant Side of Generative Zero-Shot Learning","date":"2019-04-08","arxiv_id":"1904.04092","rows_on_this_dataset":1,"code_links":1,"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":1,"official":{"repos":["lijin118/LisGAN"],"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/leveraging-the-invariant-side-of-generative#ran","syntology_url":"https://syntology.ai/paper/1904.04092","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1904.04092"}}}},{"paper":"/paper/improved-embeddings-with-easy-positive","slug":"improved-embeddings-with-easy-positive","title":"Improved Embeddings with Easy Positive Triplet Mining","date":"2019-04-08","arxiv_id":"1904.04370","rows_on_this_dataset":3,"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":0,"samples_ran_instrument_failed":1,"samples_unverified":0,"pointer_only_for_licence":1,"official":{"repos":["littleredxh/EasyPositiveHardNegative"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/improved-embeddings-with-easy-positive#ran","syntology_url":"https://syntology.ai/paper/1904.04370","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1904.04370"}}}},{"paper":"/paper/hyperbolic-image-embeddings","slug":"hyperbolic-image-embeddings","title":"Hyperbolic Image Embeddings","date":"2019-04-03","arxiv_id":"1904.02239","rows_on_this_dataset":2,"code_links":3,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":6,"samples_ran":5,"samples_constructed":0,"samples_ran_checked":3,"samples_ran_instrument_failed":2,"samples_unverified":1,"pointer_only_for_licence":0,"official":{"repos":["KhrulkovV/hyperbolic-image-embeddings","leymir/hyperbolic-image-embeddings"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":1,"ran_from_kinds":["official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/hyperbolic-image-embeddings#ran","syntology_url":"https://syntology.ai/paper/1904.02239","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1904.02239"}}}},{"paper":"/paper/hardness-aware-deep-metric-learning","slug":"hardness-aware-deep-metric-learning","title":"Hardness-Aware Deep Metric Learning","date":"2019-03-13","arxiv_id":"1903.05503","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":0,"samples_ran_checked":2,"samples_ran_instrument_failed":0,"samples_unverified":0,"pointer_only_for_licence":0,"official":{"repos":["wzzheng/HDML"],"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/hardness-aware-deep-metric-learning#ran","syntology_url":"https://syntology.ai/paper/1903.05503","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1903.05503"}}}},{"paper":"/paper/making-classification-competitive-for-deep","slug":"making-classification-competitive-for-deep","title":"Classification is a Strong Baseline for Deep Metric Learning","date":"2018-11-30","arxiv_id":"1811.12649","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":5,"samples_ran":1,"samples_constructed":0,"samples_ran_checked":1,"samples_ran_instrument_failed":0,"samples_unverified":4,"pointer_only_for_licence":0,"official":{"repos":["azgo14/classification_metric_learning"],"state":"official (archive's flag): 1 ran","n_ran":1,"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/making-classification-competitive-for-deep#ran","syntology_url":"https://syntology.ai/paper/1811.12649","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1811.12649"}}}},{"paper":"/paper/rise-randomized-input-sampling-for","slug":"rise-randomized-input-sampling-for","title":"RISE: Randomized Input Sampling for Explanation of Black-box Models","date":"2018-06-19","arxiv_id":"1806.07421","rows_on_this_dataset":1,"code_links":13,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":34,"samples_ran":32,"samples_constructed":0,"samples_ran_checked":32,"samples_ran_instrument_failed":0,"samples_unverified":2,"pointer_only_for_licence":10,"official":{"repos":["eclique/RISE"],"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/rise-randomized-input-sampling-for#ran","syntology_url":"https://syntology.ai/paper/1806.07421","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1806.07421"}}}},{"paper":"/paper/towards-faster-training-of-global-covariance","slug":"towards-faster-training-of-global-covariance","title":"Towards Faster Training of Global Covariance Pooling Networks by Iterative Matrix Square Root Normalization","date":"2017-12-04","arxiv_id":"1712.01034","rows_on_this_dataset":1,"code_links":4,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":3,"samples_ran":3,"samples_constructed":0,"samples_ran_checked":0,"samples_ran_instrument_failed":3,"samples_unverified":0,"pointer_only_for_licence":1,"official":{"repos":["jiangtaoxie/fast-MPN-COV"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official","unlocated"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/towards-faster-training-of-global-covariance#ran","syntology_url":"https://syntology.ai/paper/1712.01034","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1712.01034"}}}},{"paper":"/paper/attngan-fine-grained-text-to-image-generation","slug":"attngan-fine-grained-text-to-image-generation","title":"AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networks","date":"2017-11-28","arxiv_id":"1711.10485","rows_on_this_dataset":1,"code_links":20,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":3,"samples_ran":3,"samples_constructed":0,"samples_ran_checked":0,"samples_ran_instrument_failed":3,"samples_unverified":0,"pointer_only_for_licence":0,"official":null,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/attngan-fine-grained-text-to-image-generation#ran","syntology_url":"https://syntology.ai/paper/1711.10485","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1711.10485"}}}},{"paper":"/paper/learning-to-compare-relation-network-for-few","slug":"learning-to-compare-relation-network-for-few","title":"Learning to Compare: Relation Network for Few-Shot Learning","date":"2017-11-16","arxiv_id":"1711.06025","rows_on_this_dataset":2,"code_links":13,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":2,"samples_ran":1,"samples_constructed":0,"samples_ran_checked":0,"samples_ran_instrument_failed":1,"samples_unverified":1,"pointer_only_for_licence":1,"official":null,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/learning-to-compare-relation-network-for-few#ran","syntology_url":"https://syntology.ai/paper/1711.06025","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1711.06025"}}}},{"paper":"/paper/grad-cam-improved-visual-explanations-for","slug":"grad-cam-improved-visual-explanations-for","title":"Grad-CAM++: Improved Visual Explanations for Deep Convolutional Networks","date":"2017-10-30","arxiv_id":"1710.11063","rows_on_this_dataset":1,"code_links":24,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":7,"samples_ran":6,"samples_constructed":0,"samples_ran_checked":2,"samples_ran_instrument_failed":4,"samples_unverified":1,"pointer_only_for_licence":3,"official":{"repos":["adityac94/Grad_CAM_plus_plus"],"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/grad-cam-improved-visual-explanations-for#ran","syntology_url":"https://syntology.ai/paper/1710.11063","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1710.11063"}}}},{"paper":"/paper/stackgan-realistic-image-synthesis-with","slug":"stackgan-realistic-image-synthesis-with","title":"StackGAN++: Realistic Image Synthesis with Stacked Generative Adversarial Networks","date":"2017-10-19","arxiv_id":"1710.10916","rows_on_this_dataset":2,"code_links":16,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":31,"samples_ran":22,"samples_constructed":0,"samples_ran_checked":14,"samples_ran_instrument_failed":8,"samples_unverified":9,"pointer_only_for_licence":3,"official":{"repos":["hanzhanggit/StackGAN","hanzhanggit/StackGAN-v2"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":5,"ran_from_kinds":["listed","official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/stackgan-realistic-image-synthesis-with#ran","syntology_url":"https://syntology.ai/paper/1710.10916","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1710.10916"}}}},{"paper":"/paper/sampling-matters-in-deep-embedding-learning","slug":"sampling-matters-in-deep-embedding-learning","title":"Sampling Matters in Deep Embedding Learning","date":"2017-06-23","arxiv_id":"1706.07567","rows_on_this_dataset":1,"code_links":6,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":2,"samples_ran":2,"samples_constructed":0,"samples_ran_checked":2,"samples_ran_instrument_failed":0,"samples_unverified":0,"pointer_only_for_licence":0,"official":null,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/sampling-matters-in-deep-embedding-learning#ran","syntology_url":"https://syntology.ai/paper/1706.07567","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1706.07567"}}}},{"paper":"/paper/a-unified-approach-to-interpreting-model","slug":"a-unified-approach-to-interpreting-model","title":"A Unified Approach to Interpreting Model Predictions","date":"2017-05-22","arxiv_id":"1705.07874","rows_on_this_dataset":1,"code_links":17,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":8,"samples_ran":3,"samples_constructed":1,"samples_ran_checked":3,"samples_ran_instrument_failed":0,"samples_unverified":5,"pointer_only_for_licence":6,"official":null,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/a-unified-approach-to-interpreting-model#ran","syntology_url":"https://syntology.ai/paper/1705.07874","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1705.07874"}}}},{"paper":"/paper/prototypical-networks-for-few-shot-learning","slug":"prototypical-networks-for-few-shot-learning","title":"Prototypical Networks for Few-shot Learning","date":"2017-03-15","arxiv_id":"1703.05175","rows_on_this_dataset":1,"code_links":43,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":64,"samples_ran":50,"samples_constructed":14,"samples_ran_checked":29,"samples_ran_instrument_failed":21,"samples_unverified":14,"pointer_only_for_licence":19,"official":{"repos":["jakesnell/prototypical-networks"],"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","unlocated"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/prototypical-networks-for-few-shot-learning#ran","syntology_url":"https://syntology.ai/paper/1703.05175","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1703.05175"}}}},{"paper":"/paper/axiomatic-attribution-for-deep-networks","slug":"axiomatic-attribution-for-deep-networks","title":"Axiomatic Attribution for Deep Networks","date":"2017-03-04","arxiv_id":"1703.01365","rows_on_this_dataset":1,"code_links":40,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":56,"samples_ran":36,"samples_constructed":13,"samples_ran_checked":21,"samples_ran_instrument_failed":15,"samples_unverified":20,"pointer_only_for_licence":17,"official":{"repos":["ankurtaly/Attributions"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed","official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/axiomatic-attribution-for-deep-networks#ran","syntology_url":"https://syntology.ai/paper/1703.01365","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1703.01365"}}}},{"paper":"/paper/stackgan-text-to-photo-realistic-image","slug":"stackgan-text-to-photo-realistic-image","title":"StackGAN: Text to Photo-realistic Image Synthesis with Stacked Generative Adversarial Networks","date":"2016-12-10","arxiv_id":"1612.03242","rows_on_this_dataset":1,"code_links":21,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":31,"samples_ran":22,"samples_constructed":0,"samples_ran_checked":18,"samples_ran_instrument_failed":4,"samples_unverified":9,"pointer_only_for_licence":3,"official":{"repos":["hanzhanggit/StackGAN"],"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/stackgan-text-to-photo-realistic-image#ran","syntology_url":"https://syntology.ai/paper/1612.03242","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1612.03242"}}}},{"paper":"/paper/grad-cam-visual-explanations-from-deep","slug":"grad-cam-visual-explanations-from-deep","title":"Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization","date":"2016-10-07","arxiv_id":"1610.02391","rows_on_this_dataset":1,"code_links":126,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":141,"samples_ran":90,"samples_constructed":32,"samples_ran_checked":65,"samples_ran_instrument_failed":25,"samples_unverified":51,"pointer_only_for_licence":69,"official":{"repos":["ramprs/grad-cam"],"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/grad-cam-visual-explanations-from-deep#ran","syntology_url":"https://syntology.ai/paper/1610.02391","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1610.02391"}}}},{"paper":"/paper/infogan-interpretable-representation-learning","slug":"infogan-interpretable-representation-learning","title":"InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets","date":"2016-06-12","arxiv_id":"1606.03657","rows_on_this_dataset":1,"code_links":38,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":6,"samples_ran":5,"samples_constructed":0,"samples_ran_checked":1,"samples_ran_instrument_failed":4,"samples_unverified":1,"pointer_only_for_licence":0,"official":null,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/infogan-interpretable-representation-learning#ran","syntology_url":"https://syntology.ai/paper/1606.03657","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1606.03657"}}}},{"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/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/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"}}}}],"record_sha256":"978eaf3fc909bf958d1c85a59ca82a3ddb21054e7c8f7e131db6cc9ae0e80a42","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}