{"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":"/task/domain-generalization/papers/ran/3","list_of":"/task/domain-generalization","task":"Domain Generalization","archive":{"snapshot":"2025-07-28"},"key_notes":{"n_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'","n_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)"},"syntology_read_at":"2026-09-28T10:30:06+00:00","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 task or check it against the task'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.","page":3,"pages_in_order":3,"rows_per_page":100,"rows":[201,271],"of":271,"counts":{"archive_papers_tagged":1751,"with_a_code_link":859,"where_syntology_ran_a_sample":271,"not_listed_spam_title":0,"listed":1751,"listed_where_code_ran":271,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":233,"every_run_a_failure_of_syntologys_instrument":38,"listed_with_a_run_with_no_instrument_failure":233,"listed_every_run_a_failure_of_syntologys_instrument":38,"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 the archive tags; 'where Syntology ran a sample' counts papers with at least one harvested sample that ran, which is not a correctness claim"},"first_page":"/task/domain-generalization/papers/ran/1","prev":"/task/domain-generalization/papers/ran/2","next":null,"papers":[{"url":"/paper/ood-bench-benchmarking-and-understanding-out","slug":"ood-bench-benchmarking-and-understanding-out","title":"OoD-Bench: Quantifying and Understanding Two Dimensions of Out-of-Distribution Generalization","date":"2021-06-07","arxiv_id":"2106.03721","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/ood-bench-benchmarking-and-understanding-out#ran","syntology_url":"https://syntology.ai/paper/2106.03721","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2106.03721"}},"official":{"repos":["ynysjtu/ood_bench"],"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"]}}},{"url":"/paper/an-information-theoretic-approach-to-5","slug":"an-information-theoretic-approach-to-5","title":"An Information-theoretic Approach to Distribution Shifts","date":"2021-06-07","arxiv_id":"2106.03783","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/an-information-theoretic-approach-to-5#ran","syntology_url":"https://syntology.ai/paper/2106.03783","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2106.03783"}},"official":{"repos":["mfederici/dsit"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/counterfactual-maximum-likelihood-estimation","slug":"counterfactual-maximum-likelihood-estimation","title":"Counterfactual Maximum Likelihood Estimation for Training Deep Networks","date":"2021-06-07","arxiv_id":"2106.03831","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/counterfactual-maximum-likelihood-estimation#ran","syntology_url":"https://syntology.ai/paper/2106.03831","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2106.03831"}},"official":{"repos":["WANGXinyiLinda/CMLE"],"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"]}}},{"url":"/paper/sand-mask-an-enhanced-gradient-masking","slug":"sand-mask-an-enhanced-gradient-masking","title":"SAND-mask: An Enhanced Gradient Masking Strategy for the Discovery of Invariances in Domain Generalization","date":"2021-06-04","arxiv_id":"2106.02266","repositories_listed":2,"syntology":{"n":12,"n_ran":8,"n_constructed":0,"n_ran_checked":5,"n_instrument":3,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":11,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 3 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/sand-mask-an-enhanced-gradient-masking#ran","syntology_url":"https://syntology.ai/paper/2106.02266","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2106.02266"}},"official":{"repos":["facebookresearch/DomainBed","shahtalebi/SAND-mask"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/semi-supervised-domain-generalization-with","slug":"semi-supervised-domain-generalization-with","title":"Semi-Supervised Domain Generalization with Stochastic StyleMatch","date":"2021-06-01","arxiv_id":"2106.00592","repositories_listed":2,"syntology":{"n":4,"n_ran":4,"n_constructed":0,"n_ran_checked":2,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/semi-supervised-domain-generalization-with#ran","syntology_url":"https://syntology.ai/paper/2106.00592","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2106.00592"}},"official":{"repos":["KaiyangZhou/Dassl.pytorch","KaiyangZhou/ssdg-benchmark"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/a-fourier-based-framework-for-domain","slug":"a-fourier-based-framework-for-domain","title":"A Fourier-based Framework for Domain Generalization","date":"2021-05-24","arxiv_id":"2105.11120","repositories_listed":1,"syntology":{"n":5,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":4,"n_pointer_only":0,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 1 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/a-fourier-based-framework-for-domain#ran","syntology_url":"https://syntology.ai/paper/2105.11120","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2105.11120"}},"official":{"repos":["MediaBrain-SJTU/FACT"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/medical-image-segmentation-using-squeeze-and","slug":"medical-image-segmentation-using-squeeze-and","title":"Medical Image Segmentation Using Squeeze-and-Expansion Transformers","date":"2021-05-20","arxiv_id":"2105.09511","repositories_listed":2,"syntology":{"n":20,"n_ran":13,"n_constructed":8,"n_ran_checked":11,"n_instrument":2,"n_unverified":7,"n_honours":1,"n_violates":0,"n_no_contract":10,"n_pointer_only":20,"phrase":"13 ran (of which 8 constructed an object rather than computing a result; 11 with no instrument failure: 1 honoured, 0 violated, 10 with no contract checked; 2 where Syntology's instrument failed) · 7 unverified","sample_list":"/paper/medical-image-segmentation-using-squeeze-and#ran","syntology_url":"https://syntology.ai/paper/2105.09511","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2105.09511"}},"official":{"repos":["askerlee/segtran"],"state":"official (archive's flag): 13 ran","n_ran":13,"n_constructed":8,"n_ran_no_instrument_failure":11,"n_unverified":7,"ran_from_kinds":["official"]}}},{"url":"/paper/a-bit-more-bayesian-domain-invariant-learning","slug":"a-bit-more-bayesian-domain-invariant-learning","title":"A Bit More Bayesian: Domain-Invariant Learning with Uncertainty","date":"2021-05-09","arxiv_id":"2105.04030","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":1,"n_ran_checked":1,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":2,"phrase":"2 ran (of which 1 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/a-bit-more-bayesian-domain-invariant-learning#ran","syntology_url":"https://syntology.ai/paper/2105.04030","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2105.04030"}},"official":{"repos":["zzzx1224/A-Bit-More-Bayesian"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/learning-to-perturb-word-embeddings-for-out","slug":"learning-to-perturb-word-embeddings-for-out","title":"Learning to Perturb Word Embeddings for Out-of-distribution QA","date":"2021-05-06","arxiv_id":"2105.02692","repositories_listed":1,"syntology":{"n":10,"n_ran":8,"n_constructed":0,"n_ran_checked":4,"n_instrument":4,"n_unverified":2,"n_honours":2,"n_violates":1,"n_no_contract":1,"n_pointer_only":6,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 2 honoured, 1 violated, 1 with no contract checked; 4 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/learning-to-perturb-word-embeddings-for-out#ran","syntology_url":"https://syntology.ai/paper/2105.02692","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2105.02692"}},"official":{"repos":["seanie12/SWEP"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/selfreg-self-supervised-contrastive","slug":"selfreg-self-supervised-contrastive","title":"SelfReg: Self-supervised Contrastive Regularization for Domain Generalization","date":"2021-04-20","arxiv_id":"2104.09841","repositories_listed":2,"syntology":{"n":7,"n_ran":3,"n_constructed":0,"n_ran_checked":1,"n_instrument":2,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 2 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/selfreg-self-supervised-contrastive#ran","syntology_url":"https://syntology.ai/paper/2104.09841","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2104.09841"}},"official":{"repos":["dnap512/SelfReg"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/gradient-matching-for-domain-generalization","slug":"gradient-matching-for-domain-generalization","title":"Gradient Matching for Domain Generalization","date":"2021-04-20","arxiv_id":"2104.09937","repositories_listed":2,"syntology":{"n":5,"n_ran":5,"n_constructed":0,"n_ran_checked":3,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":5,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/gradient-matching-for-domain-generalization#ran","syntology_url":"https://syntology.ai/paper/2104.09937","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2104.09937"}},"official":{"repos":["YugeTen/fish"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/deep-stable-learning-for-out-of-distribution","slug":"deep-stable-learning-for-out-of-distribution","title":"Deep Stable Learning for Out-Of-Distribution Generalization","date":"2021-04-16","arxiv_id":"2104.07876","repositories_listed":2,"syntology":{"n":8,"n_ran":6,"n_constructed":0,"n_ran_checked":6,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":0,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/deep-stable-learning-for-out-of-distribution#ran","syntology_url":"https://syntology.ai/paper/2104.07876","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2104.07876"}},"official":{"repos":["xxgege/StableNet"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"url":"/paper/contrastive-syn-to-real-generalization-1","slug":"contrastive-syn-to-real-generalization-1","title":"Contrastive Syn-to-Real Generalization","date":"2021-04-06","arxiv_id":"2104.02290","repositories_listed":2,"syntology":{"n":4,"n_ran":3,"n_constructed":1,"n_ran_checked":3,"n_instrument":0,"n_unverified":1,"n_honours":1,"n_violates":0,"n_no_contract":2,"n_pointer_only":3,"phrase":"3 ran (of which 1 constructed an object rather than computing a result; 3 with no instrument failure: 1 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/contrastive-syn-to-real-generalization-1#ran","syntology_url":"https://syntology.ai/paper/2104.02290","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2104.02290"}},"official":{"repos":["NVlabs/CSG"],"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":["listed","official"]}}},{"url":"/paper/domain-generalization-with-mixstyle-1","slug":"domain-generalization-with-mixstyle-1","title":"Domain Generalization with MixStyle","date":"2021-04-05","arxiv_id":"2104.02008","repositories_listed":3,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/domain-generalization-with-mixstyle-1#ran","syntology_url":"https://syntology.ai/paper/2104.02008","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2104.02008"}},"official":{"repos":["KaiyangZhou/Dassl.pytorch","KaiyangZhou/mixstyle-release"],"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"]}}},{"url":"/paper/progressive-domain-expansion-network-for","slug":"progressive-domain-expansion-network-for","title":"Progressive Domain Expansion Network for Single Domain Generalization","date":"2021-03-30","arxiv_id":"2103.16050","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":2,"n_ran_checked":2,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":2,"phrase":"2 ran (of which 2 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified; every one of the 2 samples that ran constructed an object rather than computing a result","sample_list":"/paper/progressive-domain-expansion-network-for#ran","syntology_url":"https://syntology.ai/paper/2103.16050","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2103.16050"}},"official":{"repos":["lileicv/PDEN"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":2,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/robustnet-improving-domain-generalization-in","slug":"robustnet-improving-domain-generalization-in","title":"RobustNet: Improving Domain Generalization in Urban-Scene Segmentation via Instance Selective Whitening","date":"2021-03-29","arxiv_id":"2103.15597","repositories_listed":2,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":0,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/robustnet-improving-domain-generalization-in#ran","syntology_url":"https://syntology.ai/paper/2103.15597","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2103.15597"}},"official":{"repos":["shachoi/RobustNet"],"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"]}}},{"url":"/paper/dynamic-domain-adaptation-for-efficient","slug":"dynamic-domain-adaptation-for-efficient","title":"Dynamic Domain Adaptation for Efficient Inference","date":"2021-03-26","arxiv_id":"2103.16403","repositories_listed":1,"syntology":{"n":2,"n_ran":1,"n_constructed":1,"n_ran_checked":1,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":2,"phrase":"1 ran (of which 1 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified; the one sample that ran constructed an object rather than computing a result","sample_list":"/paper/dynamic-domain-adaptation-for-efficient#ran","syntology_url":"https://syntology.ai/paper/2103.16403","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2103.16403"}},"official":{"repos":["BIT-DA/DDA"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/orthogonal-projection-loss","slug":"orthogonal-projection-loss","title":"Orthogonal Projection Loss","date":"2021-03-25","arxiv_id":"2103.14021","repositories_listed":1,"syntology":{"n":14,"n_ran":8,"n_constructed":0,"n_ran_checked":7,"n_instrument":1,"n_unverified":6,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":3,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 1 where Syntology's instrument failed) · 6 unverified","sample_list":"/paper/orthogonal-projection-loss#ran","syntology_url":"https://syntology.ai/paper/2103.14021","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2103.14021"}},"official":{"repos":["kahnchana/opl"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":6,"ran_from_kinds":["official"]}}},{"url":"/paper/naturalproofs-mathematical-theorem-proving-in","slug":"naturalproofs-mathematical-theorem-proving-in","title":"NaturalProofs: Mathematical Theorem Proving in Natural Language","date":"2021-03-24","arxiv_id":"2104.01112","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/naturalproofs-mathematical-theorem-proving-in#ran","syntology_url":"https://syntology.ai/paper/2104.01112","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2104.01112"}},"official":{"repos":["wellecks/naturalproofs"],"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"]}}},{"url":"/paper/feddg-federated-domain-generalization-on","slug":"feddg-federated-domain-generalization-on","title":"FedDG: Federated Domain Generalization on Medical Image Segmentation via Episodic Learning in Continuous Frequency Space","date":"2021-03-10","arxiv_id":"2103.06030","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":0,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":2,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/feddg-federated-domain-generalization-on#ran","syntology_url":"https://syntology.ai/paper/2103.06030","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2103.06030"}},"official":{"repos":["liuquande/FedDG-ELCFS"],"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"]}}},{"url":"/paper/domain-generalization-needs-stochastic-weight","slug":"domain-generalization-needs-stochastic-weight","title":"SWAD: Domain Generalization by Seeking Flat Minima","date":"2021-02-17","arxiv_id":"2102.08604","repositories_listed":4,"syntology":{"n":3,"n_ran":2,"n_constructed":2,"n_ran_checked":2,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"2 ran (of which 2 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified; every one of the 2 samples that ran constructed an object rather than computing a result","sample_list":"/paper/domain-generalization-needs-stochastic-weight#ran","syntology_url":"https://syntology.ai/paper/2102.08604","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2102.08604"}},"official":{"repos":["khanrc/swad"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":2,"n_ran_no_instrument_failure":2,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/permuted-adain-enhancing-the-representation","slug":"permuted-adain-enhancing-the-representation","title":"Permuted AdaIN: Reducing the Bias Towards Global Statistics in Image Classification","date":"2020-10-09","arxiv_id":"2010.05785","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":0,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/permuted-adain-enhancing-the-representation#ran","syntology_url":"https://syntology.ai/paper/2010.05785","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2010.05785"}},"official":{"repos":["onuriel/PermutedAdaIN"],"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"]}}},{"url":"/paper/domain-generalization-for-medical-imaging","slug":"domain-generalization-for-medical-imaging","title":"Domain Generalization for Medical Imaging Classification with Linear-Dependency Regularization","date":"2020-09-27","arxiv_id":"2009.12829","repositories_listed":1,"syntology":{"n":4,"n_ran":4,"n_constructed":1,"n_ran_checked":2,"n_instrument":2,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":1,"n_pointer_only":4,"phrase":"4 ran (of which 1 constructed an object rather than computing a result; 2 with no instrument failure: 1 honoured, 0 violated, 1 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/domain-generalization-for-medical-imaging#ran","syntology_url":"https://syntology.ai/paper/2009.12829","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2009.12829"}},"official":{"repos":["wyf0912/LDDG"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":1,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/robust-and-generalizable-visual","slug":"robust-and-generalizable-visual","title":"Robust and Generalizable Visual Representation Learning via Random Convolutions","date":"2020-07-25","arxiv_id":"2007.13003","repositories_listed":5,"syntology":{"n":4,"n_ran":3,"n_constructed":2,"n_ran_checked":2,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":3,"phrase":"3 ran (of which 2 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/robust-and-generalizable-visual#ran","syntology_url":"https://syntology.ai/paper/2007.13003","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2007.13003"}},"official":null}},{"url":"/paper/towards-recognizing-unseen-categories-in","slug":"towards-recognizing-unseen-categories-in","title":"Towards Recognizing Unseen Categories in Unseen Domains","date":"2020-07-23","arxiv_id":"2007.12256","repositories_listed":1,"syntology":{"n":4,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 1 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/towards-recognizing-unseen-categories-in#ran","syntology_url":"https://syntology.ai/paper/2007.12256","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2007.12256"}},"official":{"repos":["mancinimassimiliano/CuMix"],"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"]}}},{"url":"/paper/dart-open-domain-structured-data-record-to","slug":"dart-open-domain-structured-data-record-to","title":"DART: Open-Domain Structured Data Record to Text Generation","date":"2020-07-06","arxiv_id":"2007.02871","repositories_listed":2,"syntology":{"n":14,"n_ran":10,"n_constructed":0,"n_ran_checked":10,"n_instrument":0,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":10,"n_pointer_only":0,"phrase":"10 ran (of which 0 constructed an object rather than computing a result; 10 with no instrument failure: 0 honoured, 0 violated, 10 with no contract checked; 0 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/dart-open-domain-structured-data-record-to#ran","syntology_url":"https://syntology.ai/paper/2007.02871","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2007.02871"}},"official":{"repos":["Yale-LILY/dart"],"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":["listed","official"]}}},{"url":"/paper/self-challenging-improves-cross-domain","slug":"self-challenging-improves-cross-domain","title":"Self-Challenging Improves Cross-Domain Generalization","date":"2020-07-05","arxiv_id":"2007.02454","repositories_listed":8,"syntology":{"n":7,"n_ran":6,"n_constructed":0,"n_ran_checked":4,"n_instrument":2,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":1,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 2 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/self-challenging-improves-cross-domain#ran","syntology_url":"https://syntology.ai/paper/2007.02454","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2007.02454"}},"official":null}},{"url":"/paper/in-search-of-lost-domain-generalization","slug":"in-search-of-lost-domain-generalization","title":"In Search of Lost Domain Generalization","date":"2020-07-02","arxiv_id":"2007.01434","repositories_listed":12,"syntology":{"n":7,"n_ran":3,"n_constructed":0,"n_ran_checked":0,"n_instrument":3,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 3 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/in-search-of-lost-domain-generalization#ran","syntology_url":"https://syntology.ai/paper/2007.01434","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2007.01434"}},"official":{"repos":["facebookresearch/DomainBed"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":3,"ran_from_kinds":["listed"]}}},{"url":"/paper/improving-robustness-against-common","slug":"improving-robustness-against-common","title":"Improving robustness against common corruptions by covariate shift adaptation","date":"2020-06-30","arxiv_id":"2006.16971","repositories_listed":2,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":0,"n_instrument":3,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 3 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/improving-robustness-against-common#ran","syntology_url":"https://syntology.ai/paper/2006.16971","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2006.16971"}},"official":{"repos":["bethgelab/robustness"],"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":["official"]}}},{"url":"/paper/the-many-faces-of-robustness-a-critical","slug":"the-many-faces-of-robustness-a-critical","title":"The Many Faces of Robustness: A Critical Analysis of Out-of-Distribution Generalization","date":"2020-06-29","arxiv_id":"2006.16241","repositories_listed":1,"syntology":{"n":14,"n_ran":14,"n_constructed":1,"n_ran_checked":2,"n_instrument":12,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"14 ran (of which 1 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 12 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/the-many-faces-of-robustness-a-critical#ran","syntology_url":"https://syntology.ai/paper/2006.16241","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2006.16241"}},"official":{"repos":["hendrycks/imagenet-r"],"state":"official (archive's flag): 14 ran","n_ran":14,"n_constructed":1,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/surpassing-real-world-source-training-data","slug":"surpassing-real-world-source-training-data","title":"Surpassing Real-World Source Training Data: Random 3D Characters for Generalizable Person Re-Identification","date":"2020-06-23","arxiv_id":"2006.12774","repositories_listed":1,"syntology":{"n":9,"n_ran":6,"n_constructed":0,"n_ran_checked":6,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":0,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/surpassing-real-world-source-training-data#ran","syntology_url":"https://syntology.ai/paper/2006.12774","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2006.12774"}},"official":{"repos":["VideoObjectSearch/RandPerson"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/frustratingly-simple-domain-generalization","slug":"frustratingly-simple-domain-generalization","title":"Frustratingly Simple Domain Generalization via Image Stylization","date":"2020-06-19","arxiv_id":"2006.11207","repositories_listed":2,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":0,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":2,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/frustratingly-simple-domain-generalization#ran","syntology_url":"https://syntology.ai/paper/2006.11207","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2006.11207"}},"official":{"repos":["GT-RIPL/DomainGeneralization-Stylization"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"url":"/paper/domain-generalization-using-causal-matching-1","slug":"domain-generalization-using-causal-matching-1","title":"Domain Generalization using Causal Matching","date":"2020-06-12","arxiv_id":"2006.07500","repositories_listed":1,"syntology":{"n":9,"n_ran":4,"n_constructed":0,"n_ran_checked":0,"n_instrument":4,"n_unverified":5,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 4 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/domain-generalization-using-causal-matching-1#ran","syntology_url":"https://syntology.ai/paper/2006.07500","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2006.07500"}},"official":{"repos":["microsoft/robustdg"],"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":["found_in_text"]}}},{"url":"/paper/towards-efficient-covid-19-ct-annotation-a","slug":"towards-efficient-covid-19-ct-annotation-a","title":"Towards Data-Efficient Learning: A Benchmark for COVID-19 CT Lung and Infection Segmentation","date":"2020-04-27","arxiv_id":"2004.12537","repositories_listed":2,"syntology":{"n":3,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/towards-efficient-covid-19-ct-annotation-a#ran","syntology_url":"https://syntology.ai/paper/2004.12537","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2004.12537"}},"official":{"repos":["HzFu/COVID19_imaging_AI_paper_list"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"url":"/paper/efficient-domain-generalization-via-common","slug":"efficient-domain-generalization-via-common","title":"Efficient Domain Generalization via Common-Specific Low-Rank Decomposition","date":"2020-03-28","arxiv_id":"2003.12815","repositories_listed":2,"syntology":{"n":11,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":7,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":0,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 7 unverified","sample_list":"/paper/efficient-domain-generalization-via-common#ran","syntology_url":"https://syntology.ai/paper/2003.12815","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2003.12815"}},"official":{"repos":["vihari/csd"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":7,"ran_from_kinds":["official"]}}},{"url":"/paper/pixel-in-pixel-net-towards-efficient-facial","slug":"pixel-in-pixel-net-towards-efficient-facial","title":"Pixel-in-Pixel Net: Towards Efficient Facial Landmark Detection in the Wild","date":"2020-03-08","arxiv_id":"2003.03771","repositories_listed":2,"syntology":{"n":17,"n_ran":15,"n_constructed":0,"n_ran_checked":13,"n_instrument":2,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":13,"n_pointer_only":0,"phrase":"15 ran (of which 0 constructed an object rather than computing a result; 13 with no instrument failure: 0 honoured, 0 violated, 13 with no contract checked; 2 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/pixel-in-pixel-net-towards-efficient-facial#ran","syntology_url":"https://syntology.ai/paper/2003.03771","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2003.03771"}},"official":{"repos":["jhb86253817/PIPNet"],"state":"official (archive's flag): 13 ran","n_ran":13,"n_constructed":0,"n_ran_no_instrument_failure":11,"n_unverified":2,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/out-of-distribution-generalization-via-risk","slug":"out-of-distribution-generalization-via-risk","title":"Out-of-Distribution Generalization via Risk Extrapolation (REx)","date":"2020-03-02","arxiv_id":"2003.00688","repositories_listed":4,"syntology":{"n":4,"n_ran":3,"n_constructed":1,"n_ran_checked":1,"n_instrument":2,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":3,"phrase":"3 ran (of which 1 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 2 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/out-of-distribution-generalization-via-risk#ran","syntology_url":"https://syntology.ai/paper/2003.00688","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2003.00688"}},"official":{"repos":["capybaralet/REx_code_release"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/on-feature-normalization-and-data","slug":"on-feature-normalization-and-data","title":"On Feature Normalization and Data Augmentation","date":"2020-02-25","arxiv_id":"2002.11102","repositories_listed":1,"syntology":{"n":14,"n_ran":6,"n_constructed":0,"n_ran_checked":3,"n_instrument":3,"n_unverified":8,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":2,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 3 where Syntology's instrument failed) · 8 unverified","sample_list":"/paper/on-feature-normalization-and-data#ran","syntology_url":"https://syntology.ai/paper/2002.11102","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2002.11102"}},"official":{"repos":["Boyiliee/MoEx"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":8,"ran_from_kinds":["official"]}}},{"url":"/paper/on-the-limits-of-cross-domain-generalization","slug":"on-the-limits-of-cross-domain-generalization","title":"On the limits of cross-domain generalization in automated X-ray prediction","date":"2020-02-06","arxiv_id":"2002.02497","repositories_listed":9,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":1,"n_instrument":2,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":3,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/on-the-limits-of-cross-domain-generalization#ran","syntology_url":"https://syntology.ai/paper/2002.02497","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2002.02497"}},"official":{"repos":["ieee8023/xray-generalization","mlmed/torchxrayvision"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/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","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":2,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","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"}},"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"]}}},{"url":"/paper/augmix-a-simple-data-processing-method-to","slug":"augmix-a-simple-data-processing-method-to","title":"AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty","date":"2019-12-05","arxiv_id":"1912.02781","repositories_listed":15,"syntology":{"n":51,"n_ran":46,"n_constructed":0,"n_ran_checked":9,"n_instrument":37,"n_unverified":5,"n_honours":4,"n_violates":2,"n_no_contract":3,"n_pointer_only":25,"phrase":"46 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 4 honoured, 2 violated, 3 with no contract checked; 37 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/augmix-a-simple-data-processing-method-to#ran","syntology_url":"https://syntology.ai/paper/1912.02781","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1912.02781"}},"official":{"repos":["google-research/augmix"],"state":"official (archive's flag): 12 ran","n_ran":12,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed","official","unlocated"]}}},{"url":"/paper/adversarial-examples-improve-image","slug":"adversarial-examples-improve-image","title":"Adversarial Examples Improve Image Recognition","date":"2019-11-21","arxiv_id":"1911.09665","repositories_listed":6,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":0,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/adversarial-examples-improve-image#ran","syntology_url":"https://syntology.ai/paper/1911.09665","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1911.09665"}},"official":{"repos":["tensorflow/tpu"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"url":"/paper/distributionally-robust-neural-networks-for","slug":"distributionally-robust-neural-networks-for","title":"Distributionally Robust Neural Networks for Group Shifts: On the Importance of Regularization for Worst-Case Generalization","date":"2019-11-20","arxiv_id":"1911.08731","repositories_listed":8,"syntology":{"n":10,"n_ran":9,"n_constructed":3,"n_ran_checked":5,"n_instrument":4,"n_unverified":1,"n_honours":2,"n_violates":0,"n_no_contract":3,"n_pointer_only":2,"phrase":"9 ran (of which 3 constructed an object rather than computing a result; 5 with no instrument failure: 2 honoured, 0 violated, 3 with no contract checked; 4 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/distributionally-robust-neural-networks-for#ran","syntology_url":"https://syntology.ai/paper/1911.08731","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1911.08731"}},"official":{"repos":["kohpangwei/group_DRO","worksheets.codalab.org/worksheets/0x621811fe446b49bb818293bae2ef88c0"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"url":"/paper/randaugment-practical-data-augmentation-with","slug":"randaugment-practical-data-augmentation-with","title":"RandAugment: Practical automated data augmentation with a reduced search space","date":"2019-09-30","arxiv_id":"1909.13719","repositories_listed":19,"syntology":{"n":65,"n_ran":58,"n_constructed":1,"n_ran_checked":7,"n_instrument":51,"n_unverified":7,"n_honours":0,"n_violates":1,"n_no_contract":6,"n_pointer_only":17,"phrase":"58 ran (of which 1 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 1 violated, 6 with no contract checked; 51 where Syntology's instrument failed) · 7 unverified","sample_list":"/paper/randaugment-practical-data-augmentation-with#ran","syntology_url":"https://syntology.ai/paper/1909.13719","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1909.13719"}},"official":null}},{"url":"/paper/natural-adversarial-examples","slug":"natural-adversarial-examples","title":"Natural Adversarial Examples","date":"2019-07-16","arxiv_id":"1907.07174","repositories_listed":3,"syntology":{"n":4,"n_ran":4,"n_constructed":0,"n_ran_checked":3,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":1,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/natural-adversarial-examples#ran","syntology_url":"https://syntology.ai/paper/1907.07174","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1907.07174"}},"official":{"repos":["hendrycks/natural-adv-examples"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/cross-domain-generalization-of-neural","slug":"cross-domain-generalization-of-neural","title":"Cross-Domain Generalization of Neural Constituency Parsers","date":"2019-07-09","arxiv_id":"1907.04347","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":0,"n_instrument":3,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":3,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 3 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/cross-domain-generalization-of-neural#ran","syntology_url":"https://syntology.ai/paper/1907.04347","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1907.04347"}},"official":{"repos":["dpfried/rnng-bert"],"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":["official"]}}},{"url":"/paper/invariant-risk-minimization","slug":"invariant-risk-minimization","title":"Invariant Risk Minimization","date":"2019-07-05","arxiv_id":"1907.02893","repositories_listed":18,"syntology":{"n":30,"n_ran":18,"n_constructed":6,"n_ran_checked":13,"n_instrument":5,"n_unverified":12,"n_honours":1,"n_violates":2,"n_no_contract":10,"n_pointer_only":11,"phrase":"18 ran (of which 6 constructed an object rather than computing a result; 13 with no instrument failure: 1 honoured, 2 violated, 10 with no contract checked; 5 where Syntology's instrument failed) · 12 unverified","sample_list":"/paper/invariant-risk-minimization#ran","syntology_url":"https://syntology.ai/paper/1907.02893","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1907.02893"}},"official":{"repos":["facebookresearch/InvariantRiskMinimization"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"url":"/paper/190513549","slug":"190513549","title":"Learning Robust Global Representations by Penalizing Local Predictive Power","date":"2019-05-29","arxiv_id":"1905.13549","repositories_listed":4,"syntology":{"n":5,"n_ran":5,"n_constructed":4,"n_ran_checked":4,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":5,"phrase":"5 ran (of which 4 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/190513549#ran","syntology_url":"https://syntology.ai/paper/1905.13549","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1905.13549"}},"official":{"repos":["HaohanWang/PAR","HaohanWang/PAR_experiments"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":2,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/efficientnet-rethinking-model-scaling-for","slug":"efficientnet-rethinking-model-scaling-for","title":"EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks","date":"2019-05-28","arxiv_id":"1905.11946","repositories_listed":144,"syntology":{"n":302,"n_ran":198,"n_constructed":73,"n_ran_checked":157,"n_instrument":41,"n_unverified":104,"n_honours":26,"n_violates":2,"n_no_contract":129,"n_pointer_only":113,"phrase":"198 ran (of which 73 constructed an object rather than computing a result; 157 with no instrument failure: 26 honoured, 2 violated, 129 with no contract checked; 41 where Syntology's instrument failed) · 104 unverified","sample_list":"/paper/efficientnet-rethinking-model-scaling-for#ran","syntology_url":"https://syntology.ai/paper/1905.11946","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1905.11946"}},"official":{"repos":["tensorflow/tpu"],"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"]}}},{"url":"/paper/cutmix-regularization-strategy-to-train","slug":"cutmix-regularization-strategy-to-train","title":"CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features","date":"2019-05-13","arxiv_id":"1905.04899","repositories_listed":30,"syntology":{"n":24,"n_ran":17,"n_constructed":6,"n_ran_checked":11,"n_instrument":6,"n_unverified":7,"n_honours":2,"n_violates":0,"n_no_contract":9,"n_pointer_only":5,"phrase":"17 ran (of which 6 constructed an object rather than computing a result; 11 with no instrument failure: 2 honoured, 0 violated, 9 with no contract checked; 6 where Syntology's instrument failed) · 7 unverified","sample_list":"/paper/cutmix-regularization-strategy-to-train#ran","syntology_url":"https://syntology.ai/paper/1905.04899","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1905.04899"}},"official":{"repos":["clovaai/CutMix-PyTorch"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"url":"/paper/adversarial-training-for-free","slug":"adversarial-training-for-free","title":"Adversarial Training for Free!","date":"2019-04-29","arxiv_id":"1904.12843","repositories_listed":6,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/adversarial-training-for-free#ran","syntology_url":"https://syntology.ai/paper/1904.12843","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1904.12843"}},"official":{"repos":["ashafahi/free_adv_train","mahyarnajibi/FreeAdversarialTraining"],"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"]}}},{"url":"/paper/190411486","slug":"190411486","title":"Making Convolutional Networks Shift-Invariant Again","date":"2019-04-25","arxiv_id":"1904.11486","repositories_listed":7,"syntology":{"n":4,"n_ran":2,"n_constructed":0,"n_ran_checked":0,"n_instrument":2,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":3,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/190411486#ran","syntology_url":"https://syntology.ai/paper/1904.11486","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1904.11486"}},"official":{"repos":["adobe/antialiased-cnns"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["unlocated"]}}},{"url":"/paper/a-closer-look-at-few-shot-classification-1","slug":"a-closer-look-at-few-shot-classification-1","title":"A Closer Look at Few-shot Classification","date":"2019-04-08","arxiv_id":"1904.04232","repositories_listed":13,"syntology":{"n":8,"n_ran":7,"n_constructed":0,"n_ran_checked":2,"n_instrument":5,"n_unverified":1,"n_honours":1,"n_violates":1,"n_no_contract":0,"n_pointer_only":7,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 1 honoured, 1 violated, 0 with no contract checked; 5 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/a-closer-look-at-few-shot-classification-1#ran","syntology_url":"https://syntology.ai/paper/1904.04232","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1904.04232"}},"official":{"repos":["wyharveychen/CloserLookFewShot"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"url":"/paper/benchmarking-neural-network-robustness-to-2","slug":"benchmarking-neural-network-robustness-to-2","title":"Benchmarking Neural Network Robustness to Common Corruptions and Perturbations","date":"2019-03-28","arxiv_id":"1903.12261","repositories_listed":14,"syntology":{"n":3,"n_ran":2,"n_constructed":0,"n_ran_checked":0,"n_instrument":2,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/benchmarking-neural-network-robustness-to-2#ran","syntology_url":"https://syntology.ai/paper/1903.12261","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1903.12261"}},"official":{"repos":["hendrycks/robustness"],"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":["official"]}}},{"url":"/paper/episodic-training-for-domain-generalization","slug":"episodic-training-for-domain-generalization","title":"Episodic Training for Domain Generalization","date":"2019-01-31","arxiv_id":"1902.00113","repositories_listed":2,"syntology":{"n":5,"n_ran":5,"n_constructed":0,"n_ran_checked":4,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":1,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/episodic-training-for-domain-generalization#ran","syntology_url":"https://syntology.ai/paper/1902.00113","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1902.00113"}},"official":{"repos":["HAHA-DL/Episodic-DG"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/a-review-of-single-source-unsupervised-domain","slug":"a-review-of-single-source-unsupervised-domain","title":"A review of domain adaptation without target labels","date":"2019-01-16","arxiv_id":"1901.05335","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/a-review-of-single-source-unsupervised-domain#ran","syntology_url":"https://syntology.ai/paper/1901.05335","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1901.05335"}},"official":null}},{"url":"/paper/bag-of-tricks-for-image-classification-with","slug":"bag-of-tricks-for-image-classification-with","title":"Bag of Tricks for Image Classification with Convolutional Neural Networks","date":"2018-12-04","arxiv_id":"1812.01187","repositories_listed":28,"syntology":{"n":15,"n_ran":11,"n_constructed":0,"n_ran_checked":9,"n_instrument":2,"n_unverified":4,"n_honours":2,"n_violates":0,"n_no_contract":7,"n_pointer_only":5,"phrase":"11 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 2 honoured, 0 violated, 7 with no contract checked; 2 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/bag-of-tricks-for-image-classification-with#ran","syntology_url":"https://syntology.ai/paper/1812.01187","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1812.01187"}},"official":{"repos":["dmlc/gluon-cv"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"url":"/paper/imagenet-trained-cnns-are-biased-towards","slug":"imagenet-trained-cnns-are-biased-towards","title":"ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness","date":"2018-11-29","arxiv_id":"1811.12231","repositories_listed":7,"syntology":{"n":6,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/imagenet-trained-cnns-are-biased-towards#ran","syntology_url":"https://syntology.ai/paper/1811.12231","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1811.12231"}},"official":{"repos":["rgeirhos/Stylized-ImageNet"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"url":"/paper/two-at-once-enhancing-learning-and","slug":"two-at-once-enhancing-learning-and","title":"Two at Once: Enhancing Learning and Generalization Capacities via IBN-Net","date":"2018-07-25","arxiv_id":"1807.09441","repositories_listed":25,"syntology":{"n":16,"n_ran":8,"n_constructed":0,"n_ran_checked":7,"n_instrument":1,"n_unverified":8,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":2,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 1 where Syntology's instrument failed) · 8 unverified","sample_list":"/paper/two-at-once-enhancing-learning-and#ran","syntology_url":"https://syntology.ai/paper/1807.09441","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1807.09441"}},"official":{"repos":["XingangPan/IBN-Net"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"url":"/paper/autoaugment-learning-augmentation-policies","slug":"autoaugment-learning-augmentation-policies","title":"AutoAugment: Learning Augmentation Policies from Data","date":"2018-05-24","arxiv_id":"1805.09501","repositories_listed":33,"syntology":{"n":43,"n_ran":25,"n_constructed":0,"n_ran_checked":22,"n_instrument":3,"n_unverified":18,"n_honours":0,"n_violates":2,"n_no_contract":20,"n_pointer_only":4,"phrase":"25 ran (of which 0 constructed an object rather than computing a result; 22 with no instrument failure: 0 honoured, 2 violated, 20 with no contract checked; 3 where Syntology's instrument failed) · 18 unverified","sample_list":"/paper/autoaugment-learning-augmentation-policies#ran","syntology_url":"https://syntology.ai/paper/1805.09501","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1805.09501"}},"official":{"repos":["tensorflow/models"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"url":"/paper/mixup-beyond-empirical-risk-minimization","slug":"mixup-beyond-empirical-risk-minimization","title":"mixup: Beyond Empirical Risk Minimization","date":"2017-10-25","arxiv_id":"1710.09412","repositories_listed":71,"syntology":{"n":47,"n_ran":36,"n_constructed":5,"n_ran_checked":16,"n_instrument":20,"n_unverified":11,"n_honours":0,"n_violates":0,"n_no_contract":16,"n_pointer_only":15,"phrase":"36 ran (of which 5 constructed an object rather than computing a result; 16 with no instrument failure: 0 honoured, 0 violated, 16 with no contract checked; 20 where Syntology's instrument failed) · 11 unverified","sample_list":"/paper/mixup-beyond-empirical-risk-minimization#ran","syntology_url":"https://syntology.ai/paper/1710.09412","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1710.09412"}},"official":{"repos":["facebookresearch/mixup-cifar10"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"url":"/paper/learning-to-generalize-meta-learning-for","slug":"learning-to-generalize-meta-learning-for","title":"Learning to Generalize: Meta-Learning for Domain Generalization","date":"2017-10-10","arxiv_id":"1710.03463","repositories_listed":5,"syntology":{"n":11,"n_ran":9,"n_constructed":0,"n_ran_checked":9,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":9,"n_pointer_only":2,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 0 honoured, 0 violated, 9 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/learning-to-generalize-meta-learning-for#ran","syntology_url":"https://syntology.ai/paper/1710.03463","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1710.03463"}},"official":null}},{"url":"/paper/deeper-broader-and-artier-domain","slug":"deeper-broader-and-artier-domain","title":"Deeper, Broader and Artier Domain Generalization","date":"2017-10-09","arxiv_id":"1710.03077","repositories_listed":6,"syntology":{"n":7,"n_ran":6,"n_constructed":0,"n_ran_checked":3,"n_instrument":3,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":2,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 3 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/deeper-broader-and-artier-domain#ran","syntology_url":"https://syntology.ai/paper/1710.03077","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1710.03077"}},"official":null}},{"url":"/paper/improved-regularization-of-convolutional","slug":"improved-regularization-of-convolutional","title":"Improved Regularization of Convolutional Neural Networks with Cutout","date":"2017-08-15","arxiv_id":"1708.04552","repositories_listed":28,"syntology":{"n":24,"n_ran":21,"n_constructed":3,"n_ran_checked":9,"n_instrument":12,"n_unverified":3,"n_honours":3,"n_violates":3,"n_no_contract":3,"n_pointer_only":5,"phrase":"21 ran (of which 3 constructed an object rather than computing a result; 9 with no instrument failure: 3 honoured, 3 violated, 3 with no contract checked; 12 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/improved-regularization-of-convolutional#ran","syntology_url":"https://syntology.ai/paper/1708.04552","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1708.04552"}},"official":{"repos":["uoguelph-mlrg/Cutout"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"url":"/paper/aggregated-residual-transformations-for-deep","slug":"aggregated-residual-transformations-for-deep","title":"Aggregated Residual Transformations for Deep Neural Networks","date":"2016-11-16","arxiv_id":"1611.05431","repositories_listed":61,"syntology":{"n":80,"n_ran":51,"n_constructed":7,"n_ran_checked":31,"n_instrument":20,"n_unverified":29,"n_honours":0,"n_violates":0,"n_no_contract":31,"n_pointer_only":13,"phrase":"51 ran (of which 7 constructed an object rather than computing a result; 31 with no instrument failure: 0 honoured, 0 violated, 31 with no contract checked; 20 where Syntology's instrument failed) · 29 unverified","sample_list":"/paper/aggregated-residual-transformations-for-deep#ran","syntology_url":"https://syntology.ai/paper/1611.05431","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1611.05431"}},"official":{"repos":["facebookresearch/ResNeXt"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"url":"/paper/domain-separation-networks","slug":"domain-separation-networks","title":"Domain Separation Networks","date":"2016-08-22","arxiv_id":"1608.06019","repositories_listed":6,"syntology":{"n":14,"n_ran":10,"n_constructed":0,"n_ran_checked":5,"n_instrument":5,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":6,"phrase":"10 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 5 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/domain-separation-networks#ran","syntology_url":"https://syntology.ai/paper/1608.06019","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1608.06019"}},"official":{"repos":["tensorflow/models"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"url":"/paper/deep-coral-correlation-alignment-for-deep","slug":"deep-coral-correlation-alignment-for-deep","title":"Deep CORAL: Correlation Alignment for Deep Domain Adaptation","date":"2016-07-06","arxiv_id":"1607.01719","repositories_listed":9,"syntology":{"n":16,"n_ran":10,"n_constructed":1,"n_ran_checked":7,"n_instrument":3,"n_unverified":6,"n_honours":4,"n_violates":0,"n_no_contract":3,"n_pointer_only":10,"phrase":"10 ran (of which 1 constructed an object rather than computing a result; 7 with no instrument failure: 4 honoured, 0 violated, 3 with no contract checked; 3 where Syntology's instrument failed) · 6 unverified","sample_list":"/paper/deep-coral-correlation-alignment-for-deep#ran","syntology_url":"https://syntology.ai/paper/1607.01719","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1607.01719"}},"official":null}},{"url":"/paper/deep-residual-learning-for-image-recognition","slug":"deep-residual-learning-for-image-recognition","title":"Deep Residual Learning for Image Recognition","date":"2015-12-10","arxiv_id":"1512.03385","repositories_listed":484,"syntology":{"n":377,"n_ran":254,"n_constructed":108,"n_ran_checked":166,"n_instrument":88,"n_unverified":123,"n_honours":3,"n_violates":1,"n_no_contract":162,"n_pointer_only":193,"phrase":"254 ran (of which 108 constructed an object rather than computing a result; 166 with no instrument failure: 3 honoured, 1 violated, 162 with no contract checked; 88 where Syntology's instrument failed) · 123 unverified","sample_list":"/paper/deep-residual-learning-for-image-recognition#ran","syntology_url":"https://syntology.ai/paper/1512.03385","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1512.03385"}},"official":{"repos":["KaimingHe/resnet-1k-layers"],"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"]}}},{"url":"/paper/invariant-models-for-causal-transfer-learning","slug":"invariant-models-for-causal-transfer-learning","title":"Invariant Models for Causal Transfer Learning","date":"2015-07-19","arxiv_id":"1507.05333","repositories_listed":1,"syntology":{"n":12,"n_ran":8,"n_constructed":0,"n_ran_checked":8,"n_instrument":0,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":8,"n_pointer_only":0,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 0 honoured, 0 violated, 8 with no contract checked; 0 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/invariant-models-for-causal-transfer-learning#ran","syntology_url":"https://syntology.ai/paper/1507.05333","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1507.05333"}},"official":{"repos":["mrojascarulla/causal_transfer_learning"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/domain-adversarial-training-of-neural","slug":"domain-adversarial-training-of-neural","title":"Domain-Adversarial Training of Neural Networks","date":"2015-05-28","arxiv_id":"1505.07818","repositories_listed":37,"syntology":{"n":52,"n_ran":35,"n_constructed":11,"n_ran_checked":18,"n_instrument":17,"n_unverified":17,"n_honours":1,"n_violates":0,"n_no_contract":17,"n_pointer_only":22,"phrase":"35 ran (of which 11 constructed an object rather than computing a result; 18 with no instrument failure: 1 honoured, 0 violated, 17 with no contract checked; 17 where Syntology's instrument failed) · 17 unverified","sample_list":"/paper/domain-adversarial-training-of-neural#ran","syntology_url":"https://syntology.ai/paper/1505.07818","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1505.07818"}},"official":null}},{"url":"/paper/very-deep-convolutional-networks-for-large","slug":"very-deep-convolutional-networks-for-large","title":"Very Deep Convolutional Networks for Large-Scale Image Recognition","date":"2014-09-04","arxiv_id":"1409.1556","repositories_listed":305,"syntology":{"n":122,"n_ran":81,"n_constructed":0,"n_ran_checked":71,"n_instrument":10,"n_unverified":41,"n_honours":0,"n_violates":0,"n_no_contract":71,"n_pointer_only":8,"phrase":"81 ran (of which 0 constructed an object rather than computing a result; 71 with no instrument failure: 0 honoured, 0 violated, 71 with no contract checked; 10 where Syntology's instrument failed) · 41 unverified","sample_list":"/paper/very-deep-convolutional-networks-for-large#ran","syntology_url":"https://syntology.ai/paper/1409.1556","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1409.1556"}},"official":null}}],"record_sha256":"f484128af0897e474110b4096fe86e9fa8cac81b32c9db033f88193530d7afe3","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}