{"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/7","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":"archive","order_definition":"repositories listed in the archive (most first), then date (newest first), then slug","page":7,"pages_in_order":18,"rows_per_page":100,"rows":[601,700],"of":1751,"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","prev":"/task/domain-generalization/papers/6","next":"/task/domain-generalization/papers/8","papers":[{"url":"/paper/augmenting-multi-turn-text-to-sql-datasets","slug":"augmenting-multi-turn-text-to-sql-datasets","title":"Augmenting Multi-Turn Text-to-SQL Datasets with Self-Play","date":"2022-10-21","arxiv_id":"2210.12096","repositories_listed":1,"syntology":null},{"url":"/paper/intra-source-style-augmentation-for-improved","slug":"intra-source-style-augmentation-for-improved","title":"Intra-Source Style Augmentation for Improved Domain Generalization","date":"2022-10-18","arxiv_id":"2210.10175","repositories_listed":1,"syntology":null},{"url":"/paper/pseudoreasoner-leveraging-pseudo-labels-for","slug":"pseudoreasoner-leveraging-pseudo-labels-for","title":"PseudoReasoner: Leveraging Pseudo Labels for Commonsense Knowledge Base Population","date":"2022-10-14","arxiv_id":"2210.07988","repositories_listed":1,"syntology":null},{"url":"/paper/m2d2-a-massively-multi-domain-language","slug":"m2d2-a-massively-multi-domain-language","title":"M2D2: A Massively Multi-domain Language Modeling Dataset","date":"2022-10-13","arxiv_id":"2210.07370","repositories_listed":1,"syntology":null},{"url":"/paper/unified-vision-and-language-prompt-learning","slug":"unified-vision-and-language-prompt-learning","title":"Unified Vision and Language Prompt Learning","date":"2022-10-13","arxiv_id":"2210.07225","repositories_listed":1,"syntology":null},{"url":"/paper/attention-diversification-for-domain","slug":"attention-diversification-for-domain","title":"Attention Diversification for Domain Generalization","date":"2022-10-09","arxiv_id":"2210.04206","repositories_listed":1,"syntology":{"n":5,"n_ran":4,"n_constructed":0,"n_ran_checked":3,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":2,"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) · 1 unverified","sample_list":"/paper/attention-diversification-for-domain#ran","syntology_url":"https://syntology.ai/paper/2210.04206","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.04206"}},"official":{"repos":["hikvision-research/domaingeneralization"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/meta-dmoe-adapting-to-domain-shift-by-meta","slug":"meta-dmoe-adapting-to-domain-shift-by-meta","title":"Meta-DMoE: Adapting to Domain Shift by Meta-Distillation from Mixture-of-Experts","date":"2022-10-08","arxiv_id":"2210.03885","repositories_listed":1,"syntology":{"n":7,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":3,"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) · 3 unverified","sample_list":"/paper/meta-dmoe-adapting-to-domain-shift-by-meta#ran","syntology_url":"https://syntology.ai/paper/2210.03885","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.03885"}},"official":{"repos":["n3il666/meta-dmoe"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/triplee-easy-domain-generalization-via","slug":"triplee-easy-domain-generalization-via","title":"TripleE: Easy Domain Generalization via Episodic Replay","date":"2022-10-04","arxiv_id":"2210.01807","repositories_listed":1,"syntology":null},{"url":"/paper/deep-spatial-domain-generalization","slug":"deep-spatial-domain-generalization","title":"Deep Spatial Domain Generalization","date":"2022-10-03","arxiv_id":"2210.00729","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/deep-spatial-domain-generalization#ran","syntology_url":"https://syntology.ai/paper/2210.00729","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.00729"}},"official":{"repos":["dyu62/deep-domain-generalization"],"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/federated-domain-generalization-for-image","slug":"federated-domain-generalization-for-image","title":"Federated Domain Generalization for Image Recognition via Cross-Client Style Transfer","date":"2022-10-03","arxiv_id":"2210.00912","repositories_listed":1,"syntology":{"n":12,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":9,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":2,"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) · 9 unverified","sample_list":"/paper/federated-domain-generalization-for-image#ran","syntology_url":"https://syntology.ai/paper/2210.00912","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.00912"}},"official":{"repos":["JeremyCJM/CCST"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":9,"ran_from_kinds":["official"]}}},{"url":"/paper/domain-generalization-for-text-classification","slug":"domain-generalization-for-text-classification","title":"Domain Generalization for Text Classification with Memory-Based Supervised Contrastive Learning","date":"2022-10-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/probing-the-robustness-of-pre-trained","slug":"probing-the-robustness-of-pre-trained","title":"Probing the Robustness of Pre-trained Language Models for Entity Matching","date":"2022-10-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/semi-supervised-domain-generalization-for","slug":"semi-supervised-domain-generalization-for","title":"Semi-Supervised Domain Generalization for Cardiac Magnetic Resonance Image Segmentation with High Quality Pseudo Labels","date":"2022-09-30","arxiv_id":"2209.15451","repositories_listed":1,"syntology":null},{"url":"/paper/domain-unified-prompt-representations-for","slug":"domain-unified-prompt-representations-for","title":"Domain-Unified Prompt Representations for Source-Free Domain Generalization","date":"2022-09-29","arxiv_id":"2209.14926","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/domain-unified-prompt-representations-for#ran","syntology_url":"https://syntology.ai/paper/2209.14926","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2209.14926"}},"official":{"repos":["muse1998/source-free-domain-generalization"],"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/high-resolution-synthesis-of-high-density","slug":"high-resolution-synthesis-of-high-density","title":"High-resolution synthesis of high-density breast mammograms: Application to improved fairness in deep learning based mass detection","date":"2022-09-20","arxiv_id":"2209.09809","repositories_listed":1,"syntology":null},{"url":"/paper/bootstrap-generalization-ability-from-loss","slug":"bootstrap-generalization-ability-from-loss","title":"Bootstrap Generalization Ability from Loss Landscape Perspective","date":"2022-09-18","arxiv_id":"2209.08473","repositories_listed":1,"syntology":null},{"url":"/paper/enhance-the-visual-representation-via","slug":"enhance-the-visual-representation-via","title":"Enhance the Visual Representation via Discrete Adversarial Training","date":"2022-09-16","arxiv_id":"2209.07735","repositories_listed":1,"syntology":{"n":11,"n_ran":10,"n_constructed":5,"n_ran_checked":7,"n_instrument":3,"n_unverified":1,"n_honours":2,"n_violates":0,"n_no_contract":5,"n_pointer_only":1,"phrase":"10 ran (of which 5 constructed an object rather than computing a result; 7 with no instrument failure: 2 honoured, 0 violated, 5 with no contract checked; 3 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/enhance-the-visual-representation-via#ran","syntology_url":"https://syntology.ai/paper/2209.07735","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2209.07735"}},"official":{"repos":["alibaba/easyrobust"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":5,"n_ran_no_instrument_failure":7,"n_unverified":1,"ran_from_kinds":["official","unlocated"]}}},{"url":"/paper/a-continual-development-methodology-for-large","slug":"a-continual-development-methodology-for-large","title":"A Continual Development Methodology for Large-scale Multitask Dynamic ML Systems","date":"2022-09-15","arxiv_id":"2209.07326","repositories_listed":1,"syntology":null},{"url":"/paper/style-variable-and-irrelevant-learning-for","slug":"style-variable-and-irrelevant-learning-for","title":"Style Variable and Irrelevant Learning for Generalizable Person Re-identification","date":"2022-09-12","arxiv_id":"2209.05235","repositories_listed":1,"syntology":null},{"url":"/paper/t-ner-an-all-round-python-library-for-1","slug":"t-ner-an-all-round-python-library-for-1","title":"T-NER: An All-Round Python Library for Transformer-based Named Entity Recognition","date":"2022-09-09","arxiv_id":"2209.12616","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":1,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":1,"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: 0 honoured, 1 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/t-ner-an-all-round-python-library-for-1#ran","syntology_url":"https://syntology.ai/paper/2209.12616","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2209.12616"}},"official":{"repos":["asahi417/tner"],"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/feta-towards-specializing-foundation-models","slug":"feta-towards-specializing-foundation-models","title":"FETA: Towards Specializing Foundation Models for Expert Task Applications","date":"2022-09-08","arxiv_id":"2209.03648","repositories_listed":1,"syntology":null},{"url":"/paper/domain-generalization-for-prostate","slug":"domain-generalization-for-prostate","title":"Domain Generalization for Prostate Segmentation in Transrectal Ultrasound Images: A Multi-center Study","date":"2022-09-05","arxiv_id":"2209.02126","repositories_listed":1,"syntology":null},{"url":"/paper/back-to-bones-rediscovering-the-role-of","slug":"back-to-bones-rediscovering-the-role-of","title":"Back-to-Bones: Rediscovering the Role of Backbones in Domain Generalization","date":"2022-09-02","arxiv_id":"2209.01121","repositories_listed":1,"syntology":null},{"url":"/paper/fs-ban-born-again-networks-for-domain","slug":"fs-ban-born-again-networks-for-domain","title":"FS-BAN: Born-Again Networks for Domain Generalization Few-Shot Classification","date":"2022-08-23","arxiv_id":"2208.10930","repositories_listed":1,"syntology":{"n":14,"n_ran":9,"n_constructed":0,"n_ran_checked":8,"n_instrument":1,"n_unverified":5,"n_honours":0,"n_violates":0,"n_no_contract":8,"n_pointer_only":6,"phrase":"9 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; 1 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/fs-ban-born-again-networks-for-domain#ran","syntology_url":"https://syntology.ai/paper/2208.10930","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2208.10930"}},"official":{"repos":["yunqing-me/Born-Again-FS"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":5,"ran_from_kinds":["official"]}}},{"url":"/paper/impash-a-novel-domain-shift-resistant","slug":"impash-a-novel-domain-shift-resistant","title":"IMPaSh: A Novel Domain-shift Resistant Representation for Colorectal Cancer Tissue Classification","date":"2022-08-23","arxiv_id":"2208.11052","repositories_listed":1,"syntology":null},{"url":"/paper/artifact-based-domain-generalization-of-skin","slug":"artifact-based-domain-generalization-of-skin","title":"Artifact-Based Domain Generalization of Skin Lesion Models","date":"2022-08-20","arxiv_id":"2208.09756","repositories_listed":1,"syntology":null},{"url":"/paper/domain-specific-risk-minimization","slug":"domain-specific-risk-minimization","title":"Domain-Specific Risk Minimization for Out-of-Distribution Generalization","date":"2022-08-18","arxiv_id":"2208.08661","repositories_listed":1,"syntology":null},{"url":"/paper/prompt-vision-transformer-for-domain","slug":"prompt-vision-transformer-for-domain","title":"Prompt Vision Transformer for Domain Generalization","date":"2022-08-18","arxiv_id":"2208.08914","repositories_listed":1,"syntology":{"n":15,"n_ran":11,"n_constructed":0,"n_ran_checked":7,"n_instrument":4,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":2,"phrase":"11 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; 4 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/prompt-vision-transformer-for-domain#ran","syntology_url":"https://syntology.ai/paper/2208.08914","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2208.08914"}},"official":{"repos":["zhengzangw/DoPrompt"],"state":"official (archive's flag): 11 ran","n_ran":11,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/semantic-self-adaptation-enhancing","slug":"semantic-self-adaptation-enhancing","title":"Semantic Self-adaptation: Enhancing Generalization with a Single Sample","date":"2022-08-10","arxiv_id":"2208.05788","repositories_listed":1,"syntology":null},{"url":"/paper/joint-covariate-alignment-and-concept","slug":"joint-covariate-alignment-and-concept","title":"Joint covariate-alignment and concept-alignment: a framework for domain generalization","date":"2022-08-01","arxiv_id":"2208.00898","repositories_listed":1,"syntology":null},{"url":"/paper/towards-domain-agnostic-depth-completion","slug":"towards-domain-agnostic-depth-completion","title":"Towards Domain-agnostic Depth Completion","date":"2022-07-29","arxiv_id":"2207.14466","repositories_listed":1,"syntology":null},{"url":"/paper/depth-field-networks-for-generalizable-multi","slug":"depth-field-networks-for-generalizable-multi","title":"Depth Field Networks for Generalizable Multi-view Scene Representation","date":"2022-07-28","arxiv_id":"2207.14287","repositories_listed":1,"syntology":null},{"url":"/paper/aadg-automatic-augmentation-for-domain","slug":"aadg-automatic-augmentation-for-domain","title":"AADG: Automatic Augmentation for Domain Generalization on Retinal Image Segmentation","date":"2022-07-27","arxiv_id":"2207.13249","repositories_listed":1,"syntology":null},{"url":"/paper/domain-decorrelation-with-potential-energy","slug":"domain-decorrelation-with-potential-energy","title":"Domain Decorrelation with Potential Energy Ranking","date":"2022-07-25","arxiv_id":"2207.12194","repositories_listed":1,"syntology":null},{"url":"/paper/domain-invariant-feature-exploration-for","slug":"domain-invariant-feature-exploration-for","title":"Domain-invariant Feature Exploration for Domain Generalization","date":"2022-07-25","arxiv_id":"2207.12020","repositories_listed":1,"syntology":{"n":6,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":2,"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) · 2 unverified","sample_list":"/paper/domain-invariant-feature-exploration-for#ran","syntology_url":"https://syntology.ai/paper/2207.12020","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2207.12020"}},"official":{"repos":["jindongwang/transferlearning"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/jigsaw-vit-learning-jigsaw-puzzles-in-vision","slug":"jigsaw-vit-learning-jigsaw-puzzles-in-vision","title":"Jigsaw-ViT: Learning Jigsaw Puzzles in Vision Transformer","date":"2022-07-25","arxiv_id":"2207.11971","repositories_listed":1,"syntology":null},{"url":"/paper/domain-generalization-for-activity","slug":"domain-generalization-for-activity","title":"Domain Generalization for Activity Recognition via Adaptive Feature Fusion","date":"2022-07-21","arxiv_id":"2207.11221","repositories_listed":1,"syntology":null},{"url":"/paper/grounding-visual-representations-with-texts","slug":"grounding-visual-representations-with-texts","title":"Grounding Visual Representations with Texts for Domain Generalization","date":"2022-07-21","arxiv_id":"2207.10285","repositories_listed":1,"syntology":{"n":8,"n_ran":4,"n_constructed":3,"n_ran_checked":3,"n_instrument":1,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":8,"phrase":"4 ran (of which 3 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) · 4 unverified","sample_list":"/paper/grounding-visual-representations-with-texts#ran","syntology_url":"https://syntology.ai/paper/2207.10285","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2207.10285"}},"official":{"repos":["mswzeus/gvrt"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":3,"n_ran_no_instrument_failure":3,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/doge-tickets-uncovering-domain-general","slug":"doge-tickets-uncovering-domain-general","title":"Doge Tickets: Uncovering Domain-general Language Models by Playing Lottery Tickets","date":"2022-07-20","arxiv_id":"2207.09638","repositories_listed":1,"syntology":null},{"url":"/paper/tackling-long-tailed-category-distribution","slug":"tackling-long-tailed-category-distribution","title":"Tackling Long-Tailed Category Distribution Under Domain Shifts","date":"2022-07-20","arxiv_id":"2207.10150","repositories_listed":1,"syntology":null},{"url":"/paper/the-caltech-fish-counting-dataset-a-benchmark","slug":"the-caltech-fish-counting-dataset-a-benchmark","title":"The Caltech Fish Counting Dataset: A Benchmark for Multiple-Object Tracking and Counting","date":"2022-07-19","arxiv_id":"2207.09295","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":0,"n_honours":1,"n_violates":2,"n_no_contract":0,"n_pointer_only":3,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 1 honoured, 2 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/the-caltech-fish-counting-dataset-a-benchmark#ran","syntology_url":"https://syntology.ai/paper/2207.09295","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2207.09295"}},"official":{"repos":["visipedia/caltech-fish-counting"],"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/improving-domain-generalization-by-learning","slug":"improving-domain-generalization-by-learning","title":"Improving Domain Generalization by Learning without Forgetting: Application in Retail Checkout","date":"2022-07-12","arxiv_id":"2207.05422","repositories_listed":1,"syntology":null},{"url":"/paper/adversarial-style-augmentation-for-domain-1","slug":"adversarial-style-augmentation-for-domain-1","title":"Adversarial Style Augmentation for Domain Generalized Urban-Scene Segmentation","date":"2022-07-11","arxiv_id":"2207.04892","repositories_listed":1,"syntology":{"n":3,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":2,"n_honours":1,"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; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/adversarial-style-augmentation-for-domain-1#ran","syntology_url":"https://syntology.ai/paper/2207.04892","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2207.04892"}},"official":null}},{"url":"/paper/style-interleaved-learning-for-generalizable","slug":"style-interleaved-learning-for-generalizable","title":"Style Interleaved Learning for Generalizable Person Re-identification","date":"2022-07-07","arxiv_id":"2207.03132","repositories_listed":1,"syntology":null},{"url":"/paper/learning-with-weak-annotations-for-robust","slug":"learning-with-weak-annotations-for-robust","title":"Learning with Weak Annotations for Robust Maritime Obstacle Detection","date":"2022-06-27","arxiv_id":"2206.13263","repositories_listed":1,"syntology":null},{"url":"/paper/gated-domain-units-for-multi-source-domain","slug":"gated-domain-units-for-multi-source-domain","title":"Gated Domain Units for Multi-source Domain Generalization","date":"2022-06-24","arxiv_id":"2206.12444","repositories_listed":1,"syntology":null},{"url":"/paper/on-certifying-and-improving-generalization-to","slug":"on-certifying-and-improving-generalization-to","title":"On Certifying and Improving Generalization to Unseen Domains","date":"2022-06-24","arxiv_id":"2206.12364","repositories_listed":1,"syntology":null},{"url":"/paper/invariant-causal-mechanisms-through-1","slug":"invariant-causal-mechanisms-through-1","title":"Invariant Causal Mechanisms through Distribution Matching","date":"2022-06-23","arxiv_id":"2206.11646","repositories_listed":1,"syntology":null},{"url":"/paper/panoramic-panoptic-segmentation-insights-into","slug":"panoramic-panoptic-segmentation-insights-into","title":"Panoramic Panoptic Segmentation: Insights Into Surrounding Parsing for Mobile Agents via Unsupervised Contrastive Learning","date":"2022-06-21","arxiv_id":"2206.10711","repositories_listed":1,"syntology":null},{"url":"/paper/mitigating-data-heterogeneity-in-federated","slug":"mitigating-data-heterogeneity-in-federated","title":"Mitigating Data Heterogeneity in Federated Learning with Data Augmentation","date":"2022-06-20","arxiv_id":"2206.09979","repositories_listed":1,"syntology":null},{"url":"/paper/self-supervised-learning-for-videos-a-survey","slug":"self-supervised-learning-for-videos-a-survey","title":"Self-Supervised Learning for Videos: A Survey","date":"2022-06-18","arxiv_id":"2207.00419","repositories_listed":1,"syntology":null},{"url":"/paper/learning-fair-representation-via","slug":"learning-fair-representation-via","title":"Learning Fair Representation via Distributional Contrastive Disentanglement","date":"2022-06-17","arxiv_id":"2206.08743","repositories_listed":1,"syntology":null},{"url":"/paper/improving-diversity-with-adversarially","slug":"improving-diversity-with-adversarially","title":"Improving Diversity with Adversarially Learned Transformations for Domain Generalization","date":"2022-06-15","arxiv_id":"2206.07736","repositories_listed":1,"syntology":null},{"url":"/paper/what-makes-domain-generalization-hard","slug":"what-makes-domain-generalization-hard","title":"Improving generalization by mimicking the human visual diet","date":"2022-06-15","arxiv_id":"2206.07802","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/what-makes-domain-generalization-hard#ran","syntology_url":"https://syntology.ai/paper/2206.07802","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2206.07802"}},"official":{"repos":["spandan-madan/human_visual_diet"],"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/slimmable-domain-adaptation-1","slug":"slimmable-domain-adaptation-1","title":"Slimmable Domain Adaptation","date":"2022-06-14","arxiv_id":"2206.06620","repositories_listed":1,"syntology":null},{"url":"/paper/synthex-scaling-up-learning-based-x-ray-image","slug":"synthex-scaling-up-learning-based-x-ray-image","title":"SyntheX: Scaling Up Learning-based X-ray Image Analysis Through In Silico Experiments","date":"2022-06-13","arxiv_id":"2206.06127","repositories_listed":1,"syntology":null},{"url":"/paper/toward-real-world-single-image-deraining-a","slug":"toward-real-world-single-image-deraining-a","title":"Toward Real-world Single Image Deraining: A New Benchmark and Beyond","date":"2022-06-11","arxiv_id":"2206.05514","repositories_listed":1,"syntology":null},{"url":"/paper/causal-balancing-for-domain-generalization","slug":"causal-balancing-for-domain-generalization","title":"Causal Balancing for Domain Generalization","date":"2022-06-10","arxiv_id":"2206.05263","repositories_listed":1,"syntology":{"n":3,"n_ran":0,"n_constructed":0,"n_ran_checked":0,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"0 ran · 3 unverified","sample_list":"/paper/causal-balancing-for-domain-generalization#ran","syntology_url":"https://syntology.ai/paper/2206.05263","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2206.05263"}},"official":{"repos":["wangxinyilinda/causal-balancing-for-domain-generalization"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":2,"ran_from_kinds":[]}}},{"url":"/paper/referring-image-matting","slug":"referring-image-matting","title":"Referring Image Matting","date":"2022-06-10","arxiv_id":"2206.05149","repositories_listed":1,"syntology":null},{"url":"/paper/one-ring-to-bring-them-all-towards-open-set","slug":"one-ring-to-bring-them-all-towards-open-set","title":"OneRing: A Simple Method for Source-free Open-partial Domain Adaptation","date":"2022-06-07","arxiv_id":"2206.03600","repositories_listed":1,"syntology":null},{"url":"/paper/dynamic-domain-generalization","slug":"dynamic-domain-generalization","title":"Dynamic Domain Generalization","date":"2022-05-27","arxiv_id":"2205.13913","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/dynamic-domain-generalization#ran","syntology_url":"https://syntology.ai/paper/2205.13913","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2205.13913"}},"official":{"repos":["metavisionlab/ddg"],"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/fedaug-reducing-the-local-learning-bias","slug":"fedaug-reducing-the-local-learning-bias","title":"FedBR: Improving Federated Learning on Heterogeneous Data via Local Learning Bias Reduction","date":"2022-05-26","arxiv_id":"2205.13462","repositories_listed":1,"syntology":{"n":15,"n_ran":10,"n_constructed":0,"n_ran_checked":6,"n_instrument":4,"n_unverified":5,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":2,"phrase":"10 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; 4 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/fedaug-reducing-the-local-learning-bias#ran","syntology_url":"https://syntology.ai/paper/2205.13462","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2205.13462"}},"official":{"repos":["lins-lab/fedbr"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":5,"ran_from_kinds":["official"]}}},{"url":"/paper/temporal-domain-generalization-with-drift","slug":"temporal-domain-generalization-with-drift","title":"Temporal Domain Generalization with Drift-Aware Dynamic Neural Networks","date":"2022-05-21","arxiv_id":"2205.10664","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":1,"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 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) · 0 unverified; the one sample that ran constructed an object rather than computing a result","sample_list":"/paper/temporal-domain-generalization-with-drift#ran","syntology_url":"https://syntology.ai/paper/2205.10664","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2205.10664"}},"official":{"repos":["baithebest/drain"],"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"]}}},{"url":"/paper/generalizing-to-evolving-domains-with-latent","slug":"generalizing-to-evolving-domains-with-latent","title":"Generalizing to Evolving Domains with Latent Structure-Aware Sequential Autoencoder","date":"2022-05-16","arxiv_id":"2205.07649","repositories_listed":1,"syntology":{"n":4,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":1,"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) · 1 unverified","sample_list":"/paper/generalizing-to-evolving-domains-with-latent#ran","syntology_url":"https://syntology.ai/paper/2205.07649","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2205.07649"}},"official":{"repos":["wonderseven/lssae"],"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"]}}},{"url":"/paper/test-time-fourier-style-calibration-for","slug":"test-time-fourier-style-calibration-for","title":"Test-time Fourier Style Calibration for Domain Generalization","date":"2022-05-13","arxiv_id":"2205.06427","repositories_listed":1,"syntology":null},{"url":"/paper/localized-adversarial-domain-generalization","slug":"localized-adversarial-domain-generalization","title":"Localized Adversarial Domain Generalization","date":"2022-05-09","arxiv_id":"2205.04114","repositories_listed":1,"syntology":null},{"url":"/paper/ellseg-gen-towards-domain-generalization-for","slug":"ellseg-gen-towards-domain-generalization-for","title":"EllSeg-Gen, towards Domain Generalization for head-mounted eyetracking","date":"2022-05-04","arxiv_id":"2205.01947","repositories_listed":1,"syntology":null},{"url":"/paper/attention-consistency-on-visual-corruptions","slug":"attention-consistency-on-visual-corruptions","title":"Attention Consistency on Visual Corruptions for Single-Source Domain Generalization","date":"2022-04-27","arxiv_id":"2204.13091","repositories_listed":1,"syntology":{"n":10,"n_ran":8,"n_constructed":0,"n_ran_checked":6,"n_instrument":2,"n_unverified":2,"n_honours":2,"n_violates":1,"n_no_contract":3,"n_pointer_only":0,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 2 honoured, 1 violated, 3 with no contract checked; 2 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/attention-consistency-on-visual-corruptions#ran","syntology_url":"https://syntology.ai/paper/2204.13091","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2204.13091"}},"official":{"repos":["explainableml/acvc"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/improving-the-generalizability-of-depression","slug":"improving-the-generalizability-of-depression","title":"Improving the Generalizability of Depression Detection by Leveraging Clinical Questionnaires","date":"2022-04-21","arxiv_id":"2204.10432","repositories_listed":1,"syntology":null},{"url":"/paper/activation-regression-for-continuous-domain","slug":"activation-regression-for-continuous-domain","title":"Activation Regression for Continuous Domain Generalization with Applications to Crop Classification","date":"2022-04-14","arxiv_id":"2204.07030","repositories_listed":1,"syntology":null},{"url":"/paper/multilingual-event-linking-to-wikidata-1","slug":"multilingual-event-linking-to-wikidata-1","title":"Multilingual Event Linking to Wikidata","date":"2022-04-13","arxiv_id":"2204.06535","repositories_listed":1,"syntology":null},{"url":"/paper/the-two-dimensions-of-worst-case-training-and","slug":"the-two-dimensions-of-worst-case-training-and","title":"The Two Dimensions of Worst-case Training and the Integrated Effect for Out-of-domain Generalization","date":"2022-04-09","arxiv_id":"2204.04384","repositories_listed":1,"syntology":{"n":4,"n_ran":2,"n_constructed":1,"n_ran_checked":1,"n_instrument":1,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":4,"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) · 2 unverified","sample_list":"/paper/the-two-dimensions-of-worst-case-training-and#ran","syntology_url":"https://syntology.ai/paper/2204.04384","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2204.04384"}},"official":{"repos":["oodbag/w2d"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/pin-the-memory-learning-to-generalize","slug":"pin-the-memory-learning-to-generalize","title":"Pin the Memory: Learning to Generalize Semantic Segmentation","date":"2022-04-07","arxiv_id":"2204.03609","repositories_listed":1,"syntology":{"n":3,"n_ran":1,"n_constructed":1,"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 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) · 2 unverified; the one sample that ran constructed an object rather than computing a result","sample_list":"/paper/pin-the-memory-learning-to-generalize#ran","syntology_url":"https://syntology.ai/paper/2204.03609","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2204.03609"}},"official":{"repos":["genie-kim/pinthememory"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/adaptive-network-combination-for-single-image","slug":"adaptive-network-combination-for-single-image","title":"Adaptive Network Combination for Single-Image Reflection Removal: A Domain Generalization Perspective","date":"2022-04-04","arxiv_id":"2204.01505","repositories_listed":1,"syntology":null},{"url":"/paper/wildnet-learning-domain-generalized-semantic","slug":"wildnet-learning-domain-generalized-semantic","title":"WildNet: Learning Domain Generalized Semantic Segmentation from the Wild","date":"2022-04-04","arxiv_id":"2204.01446","repositories_listed":1,"syntology":{"n":13,"n_ran":4,"n_constructed":0,"n_ran_checked":0,"n_instrument":4,"n_unverified":9,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":13,"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) · 9 unverified","sample_list":"/paper/wildnet-learning-domain-generalized-semantic#ran","syntology_url":"https://syntology.ai/paper/2204.01446","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2204.01446"}},"official":{"repos":["suhyeonlee/wildnet"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":8,"ran_from_kinds":["official"]}}},{"url":"/paper/improving-vision-transformers-by-revisiting","slug":"improving-vision-transformers-by-revisiting","title":"Improving Vision Transformers by Revisiting High-frequency Components","date":"2022-04-03","arxiv_id":"2204.00993","repositories_listed":1,"syntology":{"n":14,"n_ran":11,"n_constructed":0,"n_ran_checked":9,"n_instrument":2,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":9,"n_pointer_only":0,"phrase":"11 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; 2 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/improving-vision-transformers-by-revisiting#ran","syntology_url":"https://syntology.ai/paper/2204.00993","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2204.00993"}},"official":{"repos":["jiawangbai/HAT"],"state":"official (archive's flag): 11 ran","n_ran":11,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/semantic-aware-domain-generalized","slug":"semantic-aware-domain-generalized","title":"Semantic-Aware Domain Generalized Segmentation","date":"2022-04-02","arxiv_id":"2204.00822","repositories_listed":1,"syntology":{"n":5,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"2 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; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/semantic-aware-domain-generalized#ran","syntology_url":"https://syntology.ai/paper/2204.00822","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2204.00822"}},"official":{"repos":["leolyj/san-saw"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/rethinking-portrait-matting-with-privacy","slug":"rethinking-portrait-matting-with-privacy","title":"Rethinking Portrait Matting with Privacy Preserving","date":"2022-03-31","arxiv_id":"2203.16828","repositories_listed":1,"syntology":null},{"url":"/paper/causality-inspired-representation-learning","slug":"causality-inspired-representation-learning","title":"Causality Inspired Representation Learning for Domain Generalization","date":"2022-03-27","arxiv_id":"2203.14237","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":1,"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 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) · 0 unverified; the one sample that ran constructed an object rather than computing a result","sample_list":"/paper/causality-inspired-representation-learning#ran","syntology_url":"https://syntology.ai/paper/2203.14237","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.14237"}},"official":{"repos":["bit-da/cirl"],"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"]}}},{"url":"/paper/a-broad-study-of-pre-training-for-domain","slug":"a-broad-study-of-pre-training-for-domain","title":"A Broad Study of Pre-training for Domain Generalization and Adaptation","date":"2022-03-22","arxiv_id":"2203.11819","repositories_listed":1,"syntology":{"n":9,"n_ran":5,"n_constructed":0,"n_ran_checked":3,"n_instrument":2,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":2,"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) · 4 unverified","sample_list":"/paper/a-broad-study-of-pre-training-for-domain#ran","syntology_url":"https://syntology.ai/paper/2203.11819","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.11819"}},"official":{"repos":["visionlearninggroup/benchmark_domain_transfer"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/improving-generalization-in-federated","slug":"improving-generalization-in-federated","title":"Improving Generalization in Federated Learning by Seeking Flat Minima","date":"2022-03-22","arxiv_id":"2203.11834","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/improving-generalization-in-federated#ran","syntology_url":"https://syntology.ai/paper/2203.11834","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.11834"}},"official":{"repos":["debcaldarola/fedsam"],"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/multi-source-domain-adaptation-based-on","slug":"multi-source-domain-adaptation-based-on","title":"Feature Distribution Matching for Federated Domain Generalization","date":"2022-03-22","arxiv_id":"2203.11635","repositories_listed":1,"syntology":null},{"url":"/paper/domain-generalization-by-mutual-information","slug":"domain-generalization-by-mutual-information","title":"Domain Generalization by Mutual-Information Regularization with Pre-trained Models","date":"2022-03-21","arxiv_id":"2203.10789","repositories_listed":1,"syntology":{"n":13,"n_ran":11,"n_constructed":0,"n_ran_checked":9,"n_instrument":2,"n_unverified":2,"n_honours":1,"n_violates":0,"n_no_contract":8,"n_pointer_only":3,"phrase":"11 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 1 honoured, 0 violated, 8 with no contract checked; 2 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/domain-generalization-by-mutual-information#ran","syntology_url":"https://syntology.ai/paper/2203.10789","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.10789"}},"official":{"repos":["kakaobrain/miro"],"state":"official (archive's flag): 11 ran","n_ran":11,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/leveraging-expert-guided-adversarial","slug":"leveraging-expert-guided-adversarial","title":"Leveraging Expert Guided Adversarial Augmentation For Improving Generalization in Named Entity Recognition","date":"2022-03-21","arxiv_id":"2203.10693","repositories_listed":1,"syntology":null},{"url":"/paper/on-multi-domain-long-tailed-recognition","slug":"on-multi-domain-long-tailed-recognition","title":"On Multi-Domain Long-Tailed Recognition, Imbalanced Domain Generalization and Beyond","date":"2022-03-17","arxiv_id":"2203.09513","repositories_listed":1,"syntology":{"n":4,"n_ran":2,"n_constructed":1,"n_ran_checked":1,"n_instrument":1,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"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) · 2 unverified","sample_list":"/paper/on-multi-domain-long-tailed-recognition#ran","syntology_url":"https://syntology.ai/paper/2203.09513","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.09513"}},"official":{"repos":["yyzharry/multi-domain-imbalance"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/autogpart-intermediate-supervision-search-for","slug":"autogpart-intermediate-supervision-search-for","title":"AutoGPart: Intermediate Supervision Search for Generalizable 3D Part Segmentation","date":"2022-03-13","arxiv_id":"2203.06558","repositories_listed":1,"syntology":null},{"url":"/paper/domain-generalization-via-shuffled-style","slug":"domain-generalization-via-shuffled-style","title":"Domain Generalization via Shuffled Style Assembly for Face Anti-Spoofing","date":"2022-03-10","arxiv_id":"2203.05340","repositories_listed":1,"syntology":{"n":2,"n_ran":0,"n_constructed":0,"n_ran_checked":0,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"0 ran · 2 unverified","sample_list":"/paper/domain-generalization-via-shuffled-style#ran","syntology_url":"https://syntology.ai/paper/2203.05340","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.05340"}},"official":{"repos":["wangzhuo2019/ssan"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":2,"ran_from_kinds":[]}}},{"url":"/paper/student-become-decathlon-master-in-retinal","slug":"student-become-decathlon-master-in-retinal","title":"Student Becomes Decathlon Master in Retinal Vessel Segmentation via Dual-teacher Multi-target Domain Adaptation","date":"2022-03-07","arxiv_id":"2203.03631","repositories_listed":1,"syntology":null},{"url":"/paper/batchformer-learning-to-explore-sample","slug":"batchformer-learning-to-explore-sample","title":"BatchFormer: Learning to Explore Sample Relationships for Robust Representation Learning","date":"2022-03-03","arxiv_id":"2203.01522","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"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 0 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","sample_list":"/paper/batchformer-learning-to-explore-sample#ran","syntology_url":"https://syntology.ai/paper/2203.01522","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.01522"}},"official":{"repos":["zhihou7/batchformer"],"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"]}}},{"url":"/paper/bridging-the-source-to-target-gap-for-cross","slug":"bridging-the-source-to-target-gap-for-cross","title":"Bridging the Source-to-target Gap for Cross-domain Person Re-Identification with Intermediate Domains","date":"2022-03-03","arxiv_id":"2203.01682","repositories_listed":1,"syntology":null},{"url":"/paper/global-local-regularization-via","slug":"global-local-regularization-via","title":"Global-Local Regularization Via Distributional Robustness","date":"2022-03-01","arxiv_id":"2203.00553","repositories_listed":1,"syntology":null},{"url":"/paper/feddrive-generalizing-federated-learning-to","slug":"feddrive-generalizing-federated-learning-to","title":"FedDrive: Generalizing Federated Learning to Semantic Segmentation in Autonomous Driving","date":"2022-02-28","arxiv_id":"2202.13670","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/feddrive-generalizing-federated-learning-to#ran","syntology_url":"https://syntology.ai/paper/2202.13670","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2202.13670"}},"official":{"repos":["Erosinho13/FedDrive"],"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/deep-learning-for-hate-speech-detection-a","slug":"deep-learning-for-hate-speech-detection-a","title":"Deep Learning for Hate Speech Detection: A Comparative Study","date":"2022-02-19","arxiv_id":"2202.09517","repositories_listed":1,"syntology":null},{"url":"/paper/learning-to-generalize-across-domains-on-1","slug":"learning-to-generalize-across-domains-on-1","title":"Learning to Generalize across Domains on Single Test Samples","date":"2022-02-16","arxiv_id":"2202.08045","repositories_listed":1,"syntology":null},{"url":"/paper/vision-models-are-more-robust-and-fair-when","slug":"vision-models-are-more-robust-and-fair-when","title":"Vision Models Are More Robust And Fair When Pretrained On Uncurated Images Without Supervision","date":"2022-02-16","arxiv_id":"2202.08360","repositories_listed":1,"syntology":null},{"url":"/paper/scorenet-learning-non-uniform-attention-and","slug":"scorenet-learning-non-uniform-attention-and","title":"ScoreNet: Learning Non-Uniform Attention and Augmentation for Transformer-Based Histopathological Image Classification","date":"2022-02-15","arxiv_id":"2202.07570","repositories_listed":1,"syntology":null},{"url":"/paper/certifying-out-of-domain-generalization-for","slug":"certifying-out-of-domain-generalization-for","title":"Certifying Out-of-Domain Generalization for Blackbox Functions","date":"2022-02-03","arxiv_id":"2202.01679","repositories_listed":1,"syntology":null},{"url":"/paper/provable-domain-generalization-via-invariant","slug":"provable-domain-generalization-via-invariant","title":"Provable Domain Generalization via Invariant-Feature Subspace Recovery","date":"2022-01-30","arxiv_id":"2201.12919","repositories_listed":1,"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/provable-domain-generalization-via-invariant#ran","syntology_url":"https://syntology.ai/paper/2201.12919","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2201.12919"}},"official":{"repos":["haoxiang-wang/isr"],"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/itsa-an-information-theoretic-approach-to","slug":"itsa-an-information-theoretic-approach-to","title":"ITSA: An Information-Theoretic Approach to Automatic Shortcut Avoidance and Domain Generalization in Stereo Matching Networks","date":"2022-01-06","arxiv_id":"2201.02263","repositories_listed":1,"syntology":{"n":4,"n_ran":0,"n_constructed":0,"n_ran_checked":0,"n_instrument":0,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"0 ran · 4 unverified","sample_list":"/paper/itsa-an-information-theoretic-approach-to#ran","syntology_url":"https://syntology.ai/paper/2201.02263","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2201.02263"}},"official":null}},{"url":"/paper/meta-distribution-alignment-for-generalizable","slug":"meta-distribution-alignment-for-generalizable","title":"Meta Distribution Alignment for Generalizable Person Re-Identification","date":"2022-01-01","arxiv_id":null,"repositories_listed":1,"syntology":null}],"record_sha256":"86e17a191e4b0bd3278cceaeb764d9bacdbf1ddf3af2f56ac78ce956d8874b5f","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}