{"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/attribute/papers/16","list_of":"/task/attribute","task":"Attribute","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":16,"pages_in_order":54,"rows_per_page":100,"rows":[1501,1600],"of":5387,"counts":{"archive_papers_tagged":5387,"with_a_code_link":1923,"where_syntology_ran_a_sample":475,"not_listed_spam_title":0,"listed":5387,"listed_where_code_ran":475,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":387,"every_run_a_failure_of_syntologys_instrument":88,"listed_with_a_run_with_no_instrument_failure":387,"listed_every_run_a_failure_of_syntologys_instrument":88,"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/attribute","prev":"/task/attribute/papers/15","next":"/task/attribute/papers/17","papers":[{"url":"/paper/deep-clustering-based-fair-outlier-detection","slug":"deep-clustering-based-fair-outlier-detection","title":"Deep Clustering based Fair Outlier Detection","date":"2021-06-09","arxiv_id":"2106.05127","repositories_listed":1,"syntology":{"n":3,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":3,"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) · 2 unverified","sample_list":"/paper/deep-clustering-based-fair-outlier-detection#ran","syntology_url":"https://syntology.ai/paper/2106.05127","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2106.05127"}},"official":{"repos":["brandeis-machine-learning/FairOutlierDetection"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/stein-latent-optimization-for-gans","slug":"stein-latent-optimization-for-gans","title":"Stein Latent Optimization for Generative Adversarial Networks","date":"2021-06-09","arxiv_id":"2106.05319","repositories_listed":1,"syntology":{"n":6,"n_ran":4,"n_constructed":0,"n_ran_checked":3,"n_instrument":1,"n_unverified":2,"n_honours":1,"n_violates":2,"n_no_contract":0,"n_pointer_only":6,"phrase":"4 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; 1 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/stein-latent-optimization-for-gans#ran","syntology_url":"https://syntology.ai/paper/2106.05319","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2106.05319"}},"official":{"repos":["shinyflight/SLOGAN"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":2,"ran_from_kinds":["official","unlocated"]}}},{"url":"/paper/an-online-riemannian-pca-for-stochastic","slug":"an-online-riemannian-pca-for-stochastic","title":"An Online Riemannian PCA for Stochastic Canonical Correlation Analysis","date":"2021-06-08","arxiv_id":"2106.07479","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-online-riemannian-pca-for-stochastic#ran","syntology_url":"https://syntology.ai/paper/2106.07479","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2106.07479"}},"official":null}},{"url":"/paper/conversational-fashion-image-retrieval-via","slug":"conversational-fashion-image-retrieval-via","title":"Conversational Fashion Image Retrieval via Multiturn Natural Language Feedback","date":"2021-06-08","arxiv_id":"2106.04128","repositories_listed":1,"syntology":null},{"url":"/paper/interpretable-and-low-resource-entity","slug":"interpretable-and-low-resource-entity","title":"Interpretable and Low-Resource Entity Matching via Decoupling Feature Learning from Decision Making","date":"2021-06-08","arxiv_id":"2106.04174","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/interpretable-and-low-resource-entity#ran","syntology_url":"https://syntology.ai/paper/2106.04174","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2106.04174"}},"official":{"repos":["THU-KEG/HIF-KAT"],"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/low-rank-subspaces-in-gans","slug":"low-rank-subspaces-in-gans","title":"Low-Rank Subspaces in GANs","date":"2021-06-08","arxiv_id":"2106.04488","repositories_listed":1,"syntology":{"n":9,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":1,"phrase":"5 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; 0 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/low-rank-subspaces-in-gans#ran","syntology_url":"https://syntology.ai/paper/2106.04488","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2106.04488"}},"official":{"repos":["zhujiapeng/LowRankGAN"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/imgagn-imbalanced-network-embedding-via","slug":"imgagn-imbalanced-network-embedding-via","title":"ImGAGN:Imbalanced Network Embedding via Generative Adversarial Graph Networks","date":"2021-06-05","arxiv_id":"2106.02817","repositories_listed":1,"syntology":null},{"url":"/paper/variational-leakage-the-role-of-information","slug":"variational-leakage-the-role-of-information","title":"Variational Leakage: The Role of Information Complexity in Privacy Leakage","date":"2021-06-05","arxiv_id":"2106.02818","repositories_listed":1,"syntology":null},{"url":"/paper/ukiyo-e-analysis-and-creativity-with","slug":"ukiyo-e-analysis-and-creativity-with","title":"Ukiyo-e Analysis and Creativity with Attribute and Geometry Annotation","date":"2021-06-04","arxiv_id":"2106.02267","repositories_listed":1,"syntology":null},{"url":"/paper/fingerprinting-fine-tuned-language-models-in","slug":"fingerprinting-fine-tuned-language-models-in","title":"Fingerprinting Fine-tuned Language Models in the Wild","date":"2021-06-03","arxiv_id":"2106.01703","repositories_listed":1,"syntology":{"n":16,"n_ran":13,"n_constructed":0,"n_ran_checked":13,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":13,"n_pointer_only":0,"phrase":"13 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; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/fingerprinting-fine-tuned-language-models-in#ran","syntology_url":"https://syntology.ai/paper/2106.01703","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2106.01703"}},"official":{"repos":["LCS2-IIITD/ACL-FFLM"],"state":"official (archive's flag): 13 ran","n_ran":13,"n_constructed":0,"n_ran_no_instrument_failure":13,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/automatic-classification-of-attributes-in","slug":"automatic-classification-of-attributes-in","title":"Automatic Classification of Attributes in German Adjective-Noun Phrases","date":"2021-06-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/cleaning-and-structuring-the-label-space-of","slug":"cleaning-and-structuring-the-label-space-of","title":"Cleaning and Structuring the Label Space of the iMet Collection 2020","date":"2021-06-01","arxiv_id":"2106.00815","repositories_listed":1,"syntology":null},{"url":"/paper/independent-prototype-propagation-for-zero","slug":"independent-prototype-propagation-for-zero","title":"Independent Prototype Propagation for Zero-Shot Compositionality","date":"2021-06-01","arxiv_id":"2106.00305","repositories_listed":1,"syntology":null},{"url":"/paper/controllable-gradient-item-retrieval","slug":"controllable-gradient-item-retrieval","title":"Controllable Gradient Item Retrieval","date":"2021-05-31","arxiv_id":"2106.00062","repositories_listed":1,"syntology":null},{"url":"/paper/cisco-at-semeval-2021-task-5-what-s-toxic","slug":"cisco-at-semeval-2021-task-5-what-s-toxic","title":"Cisco at SemEval-2021 Task 5: What's Toxic?: Leveraging Transformers for Multiple Toxic Span Extraction from Online Comments","date":"2021-05-28","arxiv_id":"2105.13959","repositories_listed":1,"syntology":null},{"url":"/paper/fawos-fairness-aware-oversampling-algorithm","slug":"fawos-fairness-aware-oversampling-algorithm","title":"FAWOS: Fairness-Aware Oversampling Algorithm Based on Distributions of Sensitive Attributes","date":"2021-05-27","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/disentangled-face-attribute-editing-via","slug":"disentangled-face-attribute-editing-via","title":"Disentangled Face Attribute Editing via Instance-Aware Latent Space Search","date":"2021-05-26","arxiv_id":"2105.12660","repositories_listed":1,"syntology":null},{"url":"/paper/honest-but-curious-nets-sensitive-attributes","slug":"honest-but-curious-nets-sensitive-attributes","title":"Honest-but-Curious Nets: Sensitive Attributes of Private Inputs Can Be Secretly Coded into the Classifiers' Outputs","date":"2021-05-25","arxiv_id":"2105.12049","repositories_listed":1,"syntology":{"n":7,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":6,"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) · 6 unverified","sample_list":"/paper/honest-but-curious-nets-sensitive-attributes#ran","syntology_url":"https://syntology.ai/paper/2105.12049","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2105.12049"}},"official":{"repos":["mmalekzadeh/honest-but-curious-nets"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":6,"ran_from_kinds":["official"]}}},{"url":"/paper/improving-contrastive-learning-on-imbalanced-1","slug":"improving-contrastive-learning-on-imbalanced-1","title":"Improving Contrastive Learning on Imbalanced Data via Open-World Sampling","date":"2021-05-21","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/predicting-ratings-in-multi-criteria","slug":"predicting-ratings-in-multi-criteria","title":"Predicting ratings in multi-criteria recommender systems via a collective factor model","date":"2021-05-21","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/disentanglement-learning-for-variational","slug":"disentanglement-learning-for-variational","title":"Disentanglement Learning for Variational Autoencoders Applied to Audio-Visual Speech Enhancement","date":"2021-05-19","arxiv_id":"2105.08970","repositories_listed":1,"syntology":null},{"url":"/paper/high-resolution-photorealistic-image","slug":"high-resolution-photorealistic-image","title":"High-Resolution Photorealistic Image Translation in Real-Time: A Laplacian Pyramid Translation Network","date":"2021-05-19","arxiv_id":"2105.09188","repositories_listed":1,"syntology":{"n":5,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":1,"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) · 1 unverified","sample_list":"/paper/high-resolution-photorealistic-image#ran","syntology_url":"https://syntology.ai/paper/2105.09188","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2105.09188"}},"official":{"repos":["csjliang/LPTN"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/learning-language-specific-sub-network-for","slug":"learning-language-specific-sub-network-for","title":"Learning Language Specific Sub-network for Multilingual Machine Translation","date":"2021-05-19","arxiv_id":"2105.09259","repositories_listed":1,"syntology":null},{"url":"/paper/zorro-valid-sparse-and-stable-explanations-in","slug":"zorro-valid-sparse-and-stable-explanations-in","title":"Zorro: Valid, Sparse, and Stable Explanations in Graph Neural Networks","date":"2021-05-18","arxiv_id":"2105.08621","repositories_listed":1,"syntology":null},{"url":"/paper/expressive-explanations-of-dnns-by-combining","slug":"expressive-explanations-of-dnns-by-combining","title":"Expressive Explanations of DNNs by Combining Concept Analysis with ILP","date":"2021-05-16","arxiv_id":"2105.07371","repositories_listed":1,"syntology":null},{"url":"/paper/ageflow-conditional-age-progression-and","slug":"ageflow-conditional-age-progression-and","title":"AgeFlow: Conditional Age Progression and Regression with Normalizing Flows","date":"2021-05-15","arxiv_id":"2105.07239","repositories_listed":1,"syntology":{"n":23,"n_ran":21,"n_constructed":11,"n_ran_checked":14,"n_instrument":7,"n_unverified":2,"n_honours":3,"n_violates":0,"n_no_contract":11,"n_pointer_only":23,"phrase":"21 ran (of which 11 constructed an object rather than computing a result; 14 with no instrument failure: 3 honoured, 0 violated, 11 with no contract checked; 7 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/ageflow-conditional-age-progression-and#ran","syntology_url":"https://syntology.ai/paper/2105.07239","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2105.07239"}},"official":{"repos":["Hzzone/AgeFlow"],"state":"official (archive's flag): 21 ran","n_ran":21,"n_constructed":11,"n_ran_no_instrument_failure":14,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/cohort-shapley-value-for-algorithmic-fairness","slug":"cohort-shapley-value-for-algorithmic-fairness","title":"Cohort Shapley value for algorithmic fairness","date":"2021-05-15","arxiv_id":"2105.07168","repositories_listed":1,"syntology":null},{"url":"/paper/cross-modal-progressive-comprehension-for","slug":"cross-modal-progressive-comprehension-for","title":"Cross-Modal Progressive Comprehension for Referring Segmentation","date":"2021-05-15","arxiv_id":"2105.07175","repositories_listed":1,"syntology":null},{"url":"/paper/discrete-representations-in-neural-models-of","slug":"discrete-representations-in-neural-models-of","title":"Discrete representations in neural models of spoken language","date":"2021-05-12","arxiv_id":"2105.05582","repositories_listed":1,"syntology":null},{"url":"/paper/the-devil-is-in-the-details-a-diagnostic","slug":"the-devil-is-in-the-details-a-diagnostic","title":"The DEVIL is in the Details: A Diagnostic Evaluation Benchmark for Video Inpainting","date":"2021-05-11","arxiv_id":"2105.05332","repositories_listed":1,"syntology":null},{"url":"/paper/neural-graph-matching-based-collaborative","slug":"neural-graph-matching-based-collaborative","title":"Neural Graph Matching based Collaborative Filtering","date":"2021-05-10","arxiv_id":"2105.04067","repositories_listed":1,"syntology":null},{"url":"/paper/recommendations-for-item-set-completion-on","slug":"recommendations-for-item-set-completion-on","title":"Recommendations for Item Set Completion: On the Semantics of Item Co-Occurrence With Data Sparsity, Input Size, and Input Modalities","date":"2021-05-10","arxiv_id":"2105.04376","repositories_listed":1,"syntology":null},{"url":"/paper/towards-discovery-and-attribution-of-open","slug":"towards-discovery-and-attribution-of-open","title":"Towards Discovery and Attribution of Open-world GAN Generated Images","date":"2021-05-10","arxiv_id":"2105.04580","repositories_listed":1,"syntology":{"n":16,"n_ran":15,"n_constructed":0,"n_ran_checked":15,"n_instrument":0,"n_unverified":1,"n_honours":1,"n_violates":0,"n_no_contract":14,"n_pointer_only":0,"phrase":"15 ran (of which 0 constructed an object rather than computing a result; 15 with no instrument failure: 1 honoured, 0 violated, 14 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/towards-discovery-and-attribution-of-open#ran","syntology_url":"https://syntology.ai/paper/2105.04580","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2105.04580"}},"official":{"repos":["Sharath-girish/openworld-gan"],"state":"official (archive's flag): 15 ran","n_ran":15,"n_constructed":0,"n_ran_no_instrument_failure":15,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/ugrec-modeling-directed-and-undirected","slug":"ugrec-modeling-directed-and-undirected","title":"UGRec: Modeling Directed and Undirected Relations for Recommendation","date":"2021-05-10","arxiv_id":"2105.04183","repositories_listed":1,"syntology":null},{"url":"/paper/towards-novel-target-discovery-through-open","slug":"towards-novel-target-discovery-through-open","title":"Towards Novel Target Discovery Through Open-Set Domain Adaptation","date":"2021-05-06","arxiv_id":"2105.02432","repositories_listed":1,"syntology":null},{"url":"/paper/effectively-leveraging-attributes-for-visual","slug":"effectively-leveraging-attributes-for-visual","title":"Effectively Leveraging Attributes for Visual Similarity","date":"2021-05-04","arxiv_id":"2105.01695","repositories_listed":1,"syntology":null},{"url":"/paper/using-twitter-attribute-information-to","slug":"using-twitter-attribute-information-to","title":"Using Twitter Attribute Information to Predict Stock Prices","date":"2021-05-04","arxiv_id":"2105.01402","repositories_listed":1,"syntology":null},{"url":"/paper/discover-the-unknown-biased-attribute-of-an","slug":"discover-the-unknown-biased-attribute-of-an","title":"Discover the Unknown Biased Attribute of an Image Classifier","date":"2021-04-29","arxiv_id":"2104.14556","repositories_listed":1,"syntology":{"n":6,"n_ran":6,"n_constructed":0,"n_ran_checked":1,"n_instrument":5,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":6,"phrase":"6 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; 5 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/discover-the-unknown-biased-attribute-of-an#ran","syntology_url":"https://syntology.ai/paper/2104.14556","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2104.14556"}},"official":{"repos":["zhihengli-UR/discover_unknown_biases"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/scaling-and-scalability-provable-nonconvex","slug":"scaling-and-scalability-provable-nonconvex","title":"Scaling and Scalability: Provable Nonconvex Low-Rank Tensor Estimation from Incomplete Measurements","date":"2021-04-29","arxiv_id":"2104.14526","repositories_listed":1,"syntology":null},{"url":"/paper/you-can-still-achieve-fairness-without","slug":"you-can-still-achieve-fairness-without","title":"Towards Fair Classifiers Without Sensitive Attributes: Exploring Biases in Related Features","date":"2021-04-29","arxiv_id":"2104.14537","repositories_listed":1,"syntology":null},{"url":"/paper/re-don-t-judge-an-object-by-its-context","slug":"re-don-t-judge-an-object-by-its-context","title":"[Re] Don't Judge an Object by Its Context: Learning to Overcome Contextual Bias","date":"2021-04-28","arxiv_id":"2104.13582","repositories_listed":1,"syntology":null},{"url":"/paper/do-feature-attribution-methods-correctly","slug":"do-feature-attribution-methods-correctly","title":"Do Feature Attribution Methods Correctly Attribute Features?","date":"2021-04-27","arxiv_id":"2104.14403","repositories_listed":1,"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":0,"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/do-feature-attribution-methods-correctly#ran","syntology_url":"https://syntology.ai/paper/2104.14403","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2104.14403"}},"official":{"repos":["YilunZhou/feature-attribution-evaluation"],"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/eigengan-layer-wise-eigen-learning-for-gans","slug":"eigengan-layer-wise-eigen-learning-for-gans","title":"EigenGAN: Layer-Wise Eigen-Learning for GANs","date":"2021-04-26","arxiv_id":"2104.12476","repositories_listed":1,"syntology":{"n":16,"n_ran":13,"n_constructed":0,"n_ran_checked":13,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":13,"n_pointer_only":0,"phrase":"13 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; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/eigengan-layer-wise-eigen-learning-for-gans#ran","syntology_url":"https://syntology.ai/paper/2104.12476","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2104.12476"}},"official":{"repos":["LynnHo/EigenGAN-Tensorflow"],"state":"official (archive's flag): 13 ran","n_ran":13,"n_constructed":0,"n_ran_no_instrument_failure":13,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/sportscap-monocular-3d-human-motion-capture","slug":"sportscap-monocular-3d-human-motion-capture","title":"SportsCap: Monocular 3D Human Motion Capture and Fine-grained Understanding in Challenging Sports Videos","date":"2021-04-23","arxiv_id":"2104.11452","repositories_listed":1,"syntology":null},{"url":"/paper/cross-domain-and-disentangled-face","slug":"cross-domain-and-disentangled-face","title":"Cross-Domain and Disentangled Face Manipulation with 3D Guidance","date":"2021-04-22","arxiv_id":"2104.11228","repositories_listed":1,"syntology":null},{"url":"/paper/imaginative-walks-generative-random-walk","slug":"imaginative-walks-generative-random-walk","title":"Imaginative Walks: Generative Random Walk Deviation Loss for Improved Unseen Learning Representation","date":"2021-04-20","arxiv_id":"2104.09757","repositories_listed":1,"syntology":null},{"url":"/paper/revisiting-the-evaluation-of-class-activation","slug":"revisiting-the-evaluation-of-class-activation","title":"Revisiting The Evaluation of Class Activation Mapping for Explainability: A Novel Metric and Experimental Analysis","date":"2021-04-20","arxiv_id":"2104.10252","repositories_listed":1,"syntology":null},{"url":"/paper/higher-order-recurrent-space-time-transformer","slug":"higher-order-recurrent-space-time-transformer","title":"Higher Order Recurrent Space-Time Transformer for Video Action Prediction","date":"2021-04-17","arxiv_id":"2104.08665","repositories_listed":1,"syntology":null},{"url":"/paper/cross-lingual-entity-alignment-with","slug":"cross-lingual-entity-alignment-with","title":"Cross-lingual Entity Alignment with Adversarial Kernel Embedding and Adversarial Knowledge Translation","date":"2021-04-16","arxiv_id":"2104.07837","repositories_listed":1,"syntology":null},{"url":"/paper/higher-order-attribute-enhancing","slug":"higher-order-attribute-enhancing","title":"Higher-Order Attribute-Enhancing Heterogeneous Graph Neural Networks","date":"2021-04-16","arxiv_id":"2104.07892","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/higher-order-attribute-enhancing#ran","syntology_url":"https://syntology.ai/paper/2104.07892","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2104.07892"}},"official":{"repos":["RingBDStack/HAE"],"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/graph-based-person-signature-for-person-re","slug":"graph-based-person-signature-for-person-re","title":"Graph-based Person Signature for Person Re-Identifications","date":"2021-04-14","arxiv_id":"2104.06770","repositories_listed":1,"syntology":{"n":10,"n_ran":9,"n_constructed":0,"n_ran_checked":5,"n_instrument":4,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":2,"phrase":"9 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; 4 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/graph-based-person-signature-for-person-re#ran","syntology_url":"https://syntology.ai/paper/2104.06770","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2104.06770"}},"official":{"repos":["aioz-ai/CVPRW21_GPS"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/vr3dense-voxel-representation-learning-for-3d","slug":"vr3dense-voxel-representation-learning-for-3d","title":"VR3Dense: Voxel Representation Learning for 3D Object Detection and Monocular Dense Depth Reconstruction","date":"2021-04-13","arxiv_id":"2104.05932","repositories_listed":1,"syntology":null},{"url":"/paper/cope-conditional-image-generation-using","slug":"cope-conditional-image-generation-using","title":"CoPE: Conditional image generation using Polynomial Expansions","date":"2021-04-11","arxiv_id":"2104.05077","repositories_listed":1,"syntology":null},{"url":"/paper/object-priors-for-classifying-and-localizing","slug":"object-priors-for-classifying-and-localizing","title":"Object Priors for Classifying and Localizing Unseen Actions","date":"2021-04-10","arxiv_id":"2104.04715","repositories_listed":1,"syntology":null},{"url":"/paper/zs-bert-towards-zero-shot-relation-extraction","slug":"zs-bert-towards-zero-shot-relation-extraction","title":"ZS-BERT: Towards Zero-Shot Relation Extraction with Attribute Representation Learning","date":"2021-04-10","arxiv_id":"2104.04697","repositories_listed":1,"syntology":null},{"url":"/paper/multimodal-face-synthesis-from-visual","slug":"multimodal-face-synthesis-from-visual","title":"Multimodal Face Synthesis from Visual Attributes","date":"2021-04-09","arxiv_id":"2104.04362","repositories_listed":1,"syntology":null},{"url":"/paper/fine-grained-fashion-similarity-prediction-by","slug":"fine-grained-fashion-similarity-prediction-by","title":"Fine-Grained Fashion Similarity Prediction by Attribute-Specific Embedding Learning","date":"2021-04-06","arxiv_id":"2104.02429","repositories_listed":1,"syntology":null},{"url":"/paper/new-benchmarks-for-learning-on-non","slug":"new-benchmarks-for-learning-on-non","title":"New Benchmarks for Learning on Non-Homophilous Graphs","date":"2021-04-03","arxiv_id":"2104.01404","repositories_listed":1,"syntology":null},{"url":"/paper/soon-scenario-oriented-object-navigation-with","slug":"soon-scenario-oriented-object-navigation-with","title":"SOON: Scenario Oriented Object Navigation with Graph-based Exploration","date":"2021-03-31","arxiv_id":"2103.17138","repositories_listed":1,"syntology":{"n":5,"n_ran":3,"n_constructed":0,"n_ran_checked":0,"n_instrument":3,"n_unverified":2,"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) · 2 unverified","sample_list":"/paper/soon-scenario-oriented-object-navigation-with#ran","syntology_url":"https://syntology.ai/paper/2103.17138","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2103.17138"}},"official":{"repos":["zhufengdaaa/soon"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":2,"ran_from_kinds":["official","unlocated"]}}},{"url":"/paper/a-comprehensive-survey-on-knowledge-graph","slug":"a-comprehensive-survey-on-knowledge-graph","title":"A Benchmark and Comprehensive Survey on Knowledge Graph Entity Alignment via Representation Learning","date":"2021-03-28","arxiv_id":"2103.15059","repositories_listed":1,"syntology":{"n":2,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":2,"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) · 1 unverified","sample_list":"/paper/a-comprehensive-survey-on-knowledge-graph#ran","syntology_url":"https://syntology.ai/paper/2103.15059","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2103.15059"}},"official":{"repos":["ruizhang-ai/EA_for_KG"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/attrlostgan-attribute-controlled-image","slug":"attrlostgan-attribute-controlled-image","title":"AttrLostGAN: Attribute Controlled Image Synthesis from Reconfigurable Layout and Style","date":"2021-03-25","arxiv_id":"2103.13722","repositories_listed":1,"syntology":null},{"url":"/paper/matched-sample-selection-with-gans-for","slug":"matched-sample-selection-with-gans-for","title":"Matched sample selection with GANs for mitigating attribute confounding","date":"2021-03-24","arxiv_id":"2103.13455","repositories_listed":1,"syntology":null},{"url":"/paper/generalized-domain-conditioned-adaptation","slug":"generalized-domain-conditioned-adaptation","title":"Generalized Domain Conditioned Adaptation Network","date":"2021-03-23","arxiv_id":"2103.12339","repositories_listed":1,"syntology":null},{"url":"/paper/group-aware-label-transfer-for-domain","slug":"group-aware-label-transfer-for-domain","title":"Group-aware Label Transfer for Domain Adaptive Person Re-identification","date":"2021-03-23","arxiv_id":"2103.12366","repositories_listed":1,"syntology":{"n":3,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":3,"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) · 1 unverified","sample_list":"/paper/group-aware-label-transfer-for-domain#ran","syntology_url":"https://syntology.ai/paper/2103.12366","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2103.12366"}},"official":{"repos":["zkcys001/UDAStrongBaseline"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/mental-health-and-abortions-among-young-women","slug":"mental-health-and-abortions-among-young-women","title":"Mental Health and Abortions among Young Women: Time-varying Unobserved Heterogeneity, Health Behaviors, and Risky Decisions","date":"2021-03-22","arxiv_id":"2103.12159","repositories_listed":1,"syntology":null},{"url":"/paper/non-autoregressive-translation-by-learning","slug":"non-autoregressive-translation-by-learning","title":"Non-Autoregressive Translation by Learning Target Categorical Codes","date":"2021-03-21","arxiv_id":"2103.11405","repositories_listed":1,"syntology":null},{"url":"/paper/attribute-alignment-controlling-text","slug":"attribute-alignment-controlling-text","title":"Attribute Alignment: Controlling Text Generation from Pre-trained Language Models","date":"2021-03-20","arxiv_id":"2103.11070","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/attribute-alignment-controlling-text#ran","syntology_url":"https://syntology.ai/paper/2103.11070","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2103.11070"}},"official":{"repos":["diandyu/attribute_alignment"],"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/attribution-of-gradient-based-adversarial","slug":"attribution-of-gradient-based-adversarial","title":"Attribution of Gradient Based Adversarial Attacks for Reverse Engineering of Deceptions","date":"2021-03-19","arxiv_id":"2103.11002","repositories_listed":1,"syntology":null},{"url":"/paper/gradient-projection-memory-for-continual-1","slug":"gradient-projection-memory-for-continual-1","title":"Gradient Projection Memory for Continual Learning","date":"2021-03-17","arxiv_id":"2103.09762","repositories_listed":1,"syntology":{"n":4,"n_ran":4,"n_constructed":0,"n_ran_checked":1,"n_instrument":3,"n_unverified":0,"n_honours":1,"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; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 3 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/gradient-projection-memory-for-continual-1#ran","syntology_url":"https://syntology.ai/paper/2103.09762","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2103.09762"}},"official":{"repos":["sahagobinda/GPM"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/r-pointhop-a-green-accurate-and-unsupervised","slug":"r-pointhop-a-green-accurate-and-unsupervised","title":"R-PointHop: A Green, Accurate, and Unsupervised Point Cloud Registration Method","date":"2021-03-15","arxiv_id":"2103.08129","repositories_listed":1,"syntology":null},{"url":"/paper/on-the-equivalence-between-temporal-and","slug":"on-the-equivalence-between-temporal-and","title":"On the Equivalence Between Temporal and Static Graph Representations for Observational Predictions","date":"2021-03-12","arxiv_id":"2103.07016","repositories_listed":1,"syntology":null},{"url":"/paper/a-study-of-face-obfuscation-in-imagenet","slug":"a-study-of-face-obfuscation-in-imagenet","title":"A Study of Face Obfuscation in ImageNet","date":"2021-03-10","arxiv_id":"2103.06191","repositories_listed":1,"syntology":null},{"url":"/paper/range-gan-range-constrained-generative","slug":"range-gan-range-constrained-generative","title":"Range-GAN: Range-Constrained Generative Adversarial Network for Conditioned Design Synthesis","date":"2021-03-10","arxiv_id":"2103.06230","repositories_listed":1,"syntology":null},{"url":"/paper/proved-a-tool-for-graph-representation-and","slug":"proved-a-tool-for-graph-representation-and","title":"PROVED: A Tool for Graph Representation and Analysis of Uncertain Event Data","date":"2021-03-09","arxiv_id":"2103.05564","repositories_listed":1,"syntology":null},{"url":"/paper/goal-oriented-gaze-estimation-for-zero-shot","slug":"goal-oriented-gaze-estimation-for-zero-shot","title":"Goal-Oriented Gaze Estimation for Zero-Shot Learning","date":"2021-03-05","arxiv_id":"2103.03433","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/goal-oriented-gaze-estimation-for-zero-shot#ran","syntology_url":"https://syntology.ai/paper/2103.03433","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2103.03433"}},"official":{"repos":["osierboy/GEM-ZSL"],"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/attrimeter-an-attribute-guided-metric","slug":"attrimeter-an-attribute-guided-metric","title":"Explainable Person Re-Identification with Attribute-guided Metric Distillation","date":"2021-03-02","arxiv_id":"2103.01451","repositories_listed":1,"syntology":null},{"url":"/paper/counterfactual-zero-shot-and-open-set-visual","slug":"counterfactual-zero-shot-and-open-set-visual","title":"Counterfactual Zero-Shot and Open-Set Visual Recognition","date":"2021-03-01","arxiv_id":"2103.00887","repositories_listed":1,"syntology":null},{"url":"/paper/exploring-the-high-dimensional-geometry-of","slug":"exploring-the-high-dimensional-geometry-of","title":"Exploring the high dimensional geometry of HSI features","date":"2021-03-01","arxiv_id":"2103.01303","repositories_listed":1,"syntology":null},{"url":"/paper/instancerefer-cooperative-holistic","slug":"instancerefer-cooperative-holistic","title":"InstanceRefer: Cooperative Holistic Understanding for Visual Grounding on Point Clouds through Instance Multi-level Contextual Referring","date":"2021-03-01","arxiv_id":"2103.01128","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/instancerefer-cooperative-holistic#ran","syntology_url":"https://syntology.ai/paper/2103.01128","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2103.01128"}},"official":{"repos":["CurryYuan/InstanceRefer"],"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/the-facells-an-exploratory-study-about-lstm","slug":"the-facells-an-exploratory-study-about-lstm","title":"The FaCells. An Exploratory Study about LSTM Layers on Face Sketches Classifiers","date":"2021-02-22","arxiv_id":"2102.11361","repositories_listed":1,"syntology":null},{"url":"/paper/a-cooperative-memory-network-for-personalized","slug":"a-cooperative-memory-network-for-personalized","title":"A Cooperative Memory Network for Personalized Task-oriented Dialogue Systems with Incomplete User Profiles","date":"2021-02-16","arxiv_id":"2102.08322","repositories_listed":1,"syntology":null},{"url":"/paper/few-shot-graph-learning-for-molecular","slug":"few-shot-graph-learning-for-molecular","title":"Few-Shot Graph Learning for Molecular Property Prediction","date":"2021-02-16","arxiv_id":"2102.07916","repositories_listed":1,"syntology":null},{"url":"/paper/multi-attribute-enhancement-network-for","slug":"multi-attribute-enhancement-network-for","title":"Multi-Attribute Enhancement Network for Person Search","date":"2021-02-16","arxiv_id":"2102.07968","repositories_listed":1,"syntology":null},{"url":"/paper/quantifying-and-mitigating-privacy-risks-of","slug":"quantifying-and-mitigating-privacy-risks-of","title":"Quantifying and Mitigating Privacy Risks of Contrastive Learning","date":"2021-02-08","arxiv_id":"2102.04140","repositories_listed":1,"syntology":null},{"url":"/paper/converse-focus-and-guess-towards-multi","slug":"converse-focus-and-guess-towards-multi","title":"Converse, Focus and Guess -- Towards Multi-Document Driven Dialogue","date":"2021-02-04","arxiv_id":"2102.02435","repositories_listed":1,"syntology":null},{"url":"/paper/ml-doctor-holistic-risk-assessment-of","slug":"ml-doctor-holistic-risk-assessment-of","title":"ML-Doctor: Holistic Risk Assessment of Inference Attacks Against Machine Learning Models","date":"2021-02-04","arxiv_id":"2102.02551","repositories_listed":1,"syntology":{"n":3,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":2,"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) · 2 unverified","sample_list":"/paper/ml-doctor-holistic-risk-assessment-of#ran","syntology_url":"https://syntology.ai/paper/2102.02551","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2102.02551"}},"official":{"repos":["liuyugeng/ml-doctor"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/generative-multi-label-zero-shot-learning","slug":"generative-multi-label-zero-shot-learning","title":"Generative Multi-Label Zero-Shot Learning","date":"2021-01-27","arxiv_id":"2101.11606","repositories_listed":1,"syntology":null},{"url":"/paper/granular-conditional-entropy-based-attribute","slug":"granular-conditional-entropy-based-attribute","title":"Granular conditional entropy-based attribute reduction for partially labeled data with proxy labels","date":"2021-01-23","arxiv_id":"2101.09495","repositories_listed":1,"syntology":null},{"url":"/paper/eager-embedding-assisted-entity-resolution","slug":"eager-embedding-assisted-entity-resolution","title":"EAGER: Embedding-Assisted Entity Resolution for Knowledge Graphs","date":"2021-01-15","arxiv_id":"2101.06126","repositories_listed":1,"syntology":null},{"url":"/paper/practical-face-reconstruction-via","slug":"practical-face-reconstruction-via","title":"Practical Face Reconstruction via Differentiable Ray Tracing","date":"2021-01-13","arxiv_id":"2101.05356","repositories_listed":1,"syntology":null},{"url":"/paper/graph-edit-networks","slug":"graph-edit-networks","title":"Graph Edit Networks","date":"2021-01-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/knowledge-mining-and-transferring-for-domain","slug":"knowledge-mining-and-transferring-for-domain","title":"Knowledge Mining and Transferring for Domain Adaptive Object Detection","date":"2021-01-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/learning-attribute-driven-disentangled","slug":"learning-attribute-driven-disentangled","title":"Learning Attribute-Driven Disentangled Representations for Interactive Fashion Retrieval","date":"2021-01-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/multimodal-co-attention-transformer-for","slug":"multimodal-co-attention-transformer-for","title":"Multimodal Co-Attention Transformer for Survival Prediction in Gigapixel Whole Slide Images","date":"2021-01-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/c2f-fwn-coarse-to-fine-flow-warping-network","slug":"c2f-fwn-coarse-to-fine-flow-warping-network","title":"C2F-FWN: Coarse-to-Fine Flow Warping Network for Spatial-Temporal Consistent Motion Transfer","date":"2020-12-16","arxiv_id":"2012.08976","repositories_listed":1,"syntology":null},{"url":"/paper/relational-boosted-bandits","slug":"relational-boosted-bandits","title":"Relational Boosted Bandits","date":"2020-12-16","arxiv_id":"2012.09220","repositories_listed":1,"syntology":null},{"url":"/paper/deep-fusion-clustering-network","slug":"deep-fusion-clustering-network","title":"Deep Fusion Clustering Network","date":"2020-12-15","arxiv_id":"2012.09600","repositories_listed":1,"syntology":null},{"url":"/paper/improving-the-fairness-of-deep-generative","slug":"improving-the-fairness-of-deep-generative","title":"Improving the Fairness of Deep Generative Models without Retraining","date":"2020-12-09","arxiv_id":"2012.04842","repositories_listed":1,"syntology":null},{"url":"/paper/adversarial-disentanglement-of-speaker","slug":"adversarial-disentanglement-of-speaker","title":"Adversarial Disentanglement of Speaker Representation for Attribute-Driven Privacy Preservation","date":"2020-12-08","arxiv_id":"2012.04454","repositories_listed":1,"syntology":null},{"url":"/paper/detecting-32-pedestrian-attributes-for","slug":"detecting-32-pedestrian-attributes-for","title":"Detecting 32 Pedestrian Attributes for Autonomous Vehicles","date":"2020-12-04","arxiv_id":"2012.02647","repositories_listed":1,"syntology":null}],"record_sha256":"1345fdd1d8704fdbeda7888b1082c1047ffdd23d9afbd14b19b836f6f51559ea","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}