{"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/19","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":19,"pages_in_order":54,"rows_per_page":100,"rows":[1801,1900],"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/18","next":"/task/attribute/papers/20","papers":[{"url":"/paper/spatially-constrained-generative-adversarial","slug":"spatially-constrained-generative-adversarial","title":"Spatially Constrained GAN for Face and Fashion Synthesis","date":"2019-05-07","arxiv_id":"1905.02320","repositories_listed":1,"syntology":null},{"url":"/paper/learn-to-synthesize-and-synthesize-to-learn","slug":"learn-to-synthesize-and-synthesize-to-learn","title":"Learn to synthesize and synthesize to learn","date":"2019-05-01","arxiv_id":"1905.00286","repositories_listed":1,"syntology":null},{"url":"/paper/overcoming-the-disentanglement-vs","slug":"overcoming-the-disentanglement-vs","title":"Overcoming the Disentanglement vs Reconstruction Trade-off via Jacobian Supervision","date":"2019-05-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/attribute-guided-unpaired-image-to-image","slug":"attribute-guided-unpaired-image-to-image","title":"Attribute Guided Unpaired Image-to-Image Translation with Semi-supervised Learning","date":"2019-04-29","arxiv_id":"1904.12428","repositories_listed":1,"syntology":null},{"url":"/paper/look-whos-talking-inferring-speaker","slug":"look-whos-talking-inferring-speaker","title":"Look Who's Talking: Inferring Speaker Attributes from Personal Longitudinal Dialog","date":"2019-04-25","arxiv_id":"1904.11610","repositories_listed":1,"syntology":null},{"url":"/paper/listening-between-the-lines-learning-personal","slug":"listening-between-the-lines-learning-personal","title":"Listening between the Lines: Learning Personal Attributes from Conversations","date":"2019-04-24","arxiv_id":"1904.10887","repositories_listed":1,"syntology":null},{"url":"/paper/salient-object-detection-in-the-deep-learning","slug":"salient-object-detection-in-the-deep-learning","title":"Salient Object Detection in the Deep Learning Era: An In-Depth Survey","date":"2019-04-19","arxiv_id":"1904.09146","repositories_listed":1,"syntology":null},{"url":"/paper/node2bits-compact-time-and-attribute-aware","slug":"node2bits-compact-time-and-attribute-aware","title":"node2bits: Compact Time- and Attribute-aware Node Representations for User Stitching","date":"2019-04-18","arxiv_id":"1904.08572","repositories_listed":1,"syntology":null},{"url":"/paper/interpreting-adversarial-examples-with","slug":"interpreting-adversarial-examples-with","title":"Interpreting Adversarial Examples with Attributes","date":"2019-04-17","arxiv_id":"1904.08279","repositories_listed":1,"syntology":null},{"url":"/paper/towards-photographic-image-manipulation-with","slug":"towards-photographic-image-manipulation-with","title":"Towards Photographic Image Manipulation with Balanced Growing of Generative Autoencoders","date":"2019-04-12","arxiv_id":"1904.06145","repositories_listed":1,"syntology":null},{"url":"/paper/tafe-net-task-aware-feature-embeddings-for-1","slug":"tafe-net-task-aware-feature-embeddings-for-1","title":"TAFE-Net: Task-Aware Feature Embeddings for Low Shot Learning","date":"2019-04-11","arxiv_id":"1904.05967","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/tafe-net-task-aware-feature-embeddings-for-1#ran","syntology_url":"https://syntology.ai/paper/1904.05967","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1904.05967"}},"official":{"repos":["ucbdrive/tafe-net"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/adversarial-learning-of-disentangled-and","slug":"adversarial-learning-of-disentangled-and","title":"Adversarial Learning of Disentangled and Generalizable Representations for Visual Attributes","date":"2019-04-09","arxiv_id":"1904.04772","repositories_listed":1,"syntology":null},{"url":"/paper/modularized-textual-grounding-for","slug":"modularized-textual-grounding-for","title":"Modularized Textual Grounding for Counterfactual Resilience","date":"2019-04-07","arxiv_id":"1904.03589","repositories_listed":1,"syntology":null},{"url":"/paper/predicting-multiple-demographic-attributes","slug":"predicting-multiple-demographic-attributes","title":"Predicting Multiple Demographic Attributes with Task Specific Embedding Transformation and Attention Network","date":"2019-03-25","arxiv_id":"1903.10144","repositories_listed":1,"syntology":null},{"url":"/paper/aloha-auxiliary-loss-optimization-for","slug":"aloha-auxiliary-loss-optimization-for","title":"ALOHA: Auxiliary Loss Optimization for Hypothesis Augmentation","date":"2019-03-13","arxiv_id":"1903.05700","repositories_listed":1,"syntology":null},{"url":"/paper/shape2motion-joint-analysis-of-motion-parts","slug":"shape2motion-joint-analysis-of-motion-parts","title":"Shape2Motion: Joint Analysis of Motion Parts and Attributes from 3D Shapes","date":"2019-03-10","arxiv_id":"1903.03911","repositories_listed":1,"syntology":null},{"url":"/paper/zero-shot-learning-of-3d-point-cloud-objects","slug":"zero-shot-learning-of-3d-point-cloud-objects","title":"Zero-shot Learning of 3D Point Cloud Objects","date":"2019-02-27","arxiv_id":"1902.10272","repositories_listed":1,"syntology":null},{"url":"/paper/disentangled-representation-learning-for-3d","slug":"disentangled-representation-learning-for-3d","title":"Disentangled Representation Learning for 3D Face Shape","date":"2019-02-26","arxiv_id":"1902.09887","repositories_listed":1,"syntology":{"n":3,"n_ran":2,"n_constructed":0,"n_ran_checked":0,"n_instrument":2,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":3,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/disentangled-representation-learning-for-3d#ran","syntology_url":"https://syntology.ai/paper/1902.09887","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1902.09887"}},"official":{"repos":["zihangJiang/DR-Learning-for-3D-Face"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/wasserstein-barycenter-model-ensembling-1","slug":"wasserstein-barycenter-model-ensembling-1","title":"Wasserstein Barycenter Model Ensembling","date":"2019-02-13","arxiv_id":"1902.04999","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/wasserstein-barycenter-model-ensembling-1#ran","syntology_url":"https://syntology.ai/paper/1902.04999","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1902.04999"}},"official":null}},{"url":"/paper/unpaired-image-to-image-translation-with","slug":"unpaired-image-to-image-translation-with","title":"Exploring Explicit Domain Supervision for Latent Space Disentanglement in Unpaired Image-to-Image Translation","date":"2019-02-11","arxiv_id":"1902.03782","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":1,"n_no_contract":0,"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, 1 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/unpaired-image-to-image-translation-with#ran","syntology_url":"https://syntology.ai/paper/1902.03782","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1902.03782"}},"official":{"repos":["linjx-ustc1106/DosGAN-PyTorch"],"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/attribute-guided-sketch-generation","slug":"attribute-guided-sketch-generation","title":"Attribute-Guided Sketch Generation","date":"2019-01-28","arxiv_id":"1901.09774","repositories_listed":1,"syntology":null},{"url":"/paper/can-adversarial-networks-hallucinate-occluded","slug":"can-adversarial-networks-hallucinate-occluded","title":"Can Adversarial Networks Hallucinate Occluded People With a Plausible Aspect?","date":"2019-01-23","arxiv_id":"1901.08097","repositories_listed":1,"syntology":null},{"url":"/paper/pedestrian-attribute-recognition-a-survey","slug":"pedestrian-attribute-recognition-a-survey","title":"Pedestrian Attribute Recognition: A Survey","date":"2019-01-22","arxiv_id":"1901.07474","repositories_listed":1,"syntology":null},{"url":"/paper/attributed-network-embedding-via-subspace","slug":"attributed-network-embedding-via-subspace","title":"Attributed Network Embedding via Subspace Discovery","date":"2019-01-14","arxiv_id":"1901.04095","repositories_listed":1,"syntology":null},{"url":"/paper/search-efficient-binary-network-embedding","slug":"search-efficient-binary-network-embedding","title":"Search Efficient Binary Network Embedding","date":"2019-01-14","arxiv_id":"1901.04097","repositories_listed":1,"syntology":null},{"url":"/paper/attribute-aware-attention-model-for-fine","slug":"attribute-aware-attention-model-for-fine","title":"Attribute-Aware Attention Model for Fine-grained Representation Learning","date":"2019-01-02","arxiv_id":"1901.00392","repositories_listed":1,"syntology":null},{"url":"/paper/learning-latent-subspaces-in-variational","slug":"learning-latent-subspaces-in-variational","title":"Learning Latent Subspaces in Variational Autoencoders","date":"2018-12-14","arxiv_id":"1812.06190","repositories_listed":1,"syntology":null},{"url":"/paper/enhancing-perceptual-attributes-with-bayesian","slug":"enhancing-perceptual-attributes-with-bayesian","title":"Enhancing Perceptual Attributes with Bayesian Style Generation","date":"2018-12-03","arxiv_id":"1812.00717","repositories_listed":1,"syntology":null},{"url":"/paper/attributed-network-embedding-for-incomplete","slug":"attributed-network-embedding-for-incomplete","title":"Attributed Network Embedding for Incomplete Attributed Networks","date":"2018-11-28","arxiv_id":"1811.11728","repositories_listed":1,"syntology":null},{"url":"/paper/changing-the-image-memorability-from-basic","slug":"changing-the-image-memorability-from-basic","title":"Changing the Image Memorability: From Basic Photo Editing to GANs","date":"2018-11-09","arxiv_id":"1811.03825","repositories_listed":1,"syntology":null},{"url":"/paper/triple-consistency-loss-for-pairing","slug":"triple-consistency-loss-for-pairing","title":"Triple consistency loss for pairing distributions in GAN-based face synthesis","date":"2018-11-08","arxiv_id":"1811.03492","repositories_listed":1,"syntology":null},{"url":"/paper/content-preserving-text-generation-with","slug":"content-preserving-text-generation-with","title":"Content preserving text generation with attribute controls","date":"2018-11-03","arxiv_id":"1811.01135","repositories_listed":1,"syntology":null},{"url":"/paper/tallyqa-answering-complex-counting-questions","slug":"tallyqa-answering-complex-counting-questions","title":"TallyQA: Answering Complex Counting Questions","date":"2018-10-29","arxiv_id":"1810.12440","repositories_listed":1,"syntology":null},{"url":"/paper/attacks-meet-interpretability-attribute","slug":"attacks-meet-interpretability-attribute","title":"Attacks Meet Interpretability: Attribute-steered Detection of Adversarial Samples","date":"2018-10-27","arxiv_id":"1810.11580","repositories_listed":1,"syntology":{"n":9,"n_ran":8,"n_constructed":0,"n_ran_checked":8,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":8,"n_pointer_only":0,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 0 honoured, 0 violated, 8 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/attacks-meet-interpretability-attribute#ran","syntology_url":"https://syntology.ai/paper/1810.11580","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1810.11580"}},"official":{"repos":["AmIAttribute/AmI"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/exploring-correlations-in-multiple-facial","slug":"exploring-correlations-in-multiple-facial","title":"Exploring Correlations in Multiple Facial Attributes through Graph Attention Network","date":"2018-10-22","arxiv_id":"1810.09162","repositories_listed":1,"syntology":null},{"url":"/paper/hunting-for-discriminatory-proxies-in-linear","slug":"hunting-for-discriminatory-proxies-in-linear","title":"Hunting for Discriminatory Proxies in Linear Regression Models","date":"2018-10-16","arxiv_id":"1810.07155","repositories_listed":1,"syntology":null},{"url":"/paper/neural-styling-for-interpretable-fair","slug":"neural-styling-for-interpretable-fair","title":"Discovering Fair Representations in the Data Domain","date":"2018-10-15","arxiv_id":"1810.06755","repositories_listed":1,"syntology":null},{"url":"/paper/cross-lingual-knowledge-graph-alignment-via","slug":"cross-lingual-knowledge-graph-alignment-via","title":"Cross-lingual Knowledge Graph Alignment via Graph Convolutional Networks","date":"2018-10-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/increasing-in-class-similarity-by","slug":"increasing-in-class-similarity-by","title":"Increasing In-Class Similarity by Retrofitting Embeddings with Demographic Information","date":"2018-10-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/weeding-out-conventionalized-metaphors-a","slug":"weeding-out-conventionalized-metaphors-a","title":"Weeding out Conventionalized Metaphors: A Corpus of Novel Metaphor Annotations","date":"2018-10-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/non-linear-attributed-graph-clustering-by","slug":"non-linear-attributed-graph-clustering-by","title":"Non-linear Attributed Graph Clustering by Symmetric NMF with PU Learning","date":"2018-09-21","arxiv_id":"1810.00946","repositories_listed":1,"syntology":null},{"url":"/paper/soft-phoc-descriptor-for-end-to-end-word","slug":"soft-phoc-descriptor-for-end-to-end-word","title":"Soft-PHOC Descriptor for End-to-End Word Spotting in Egocentric Scene Images","date":"2018-09-04","arxiv_id":"1809.00854","repositories_listed":1,"syntology":null},{"url":"/paper/efficient-relative-attribute-learning-using","slug":"efficient-relative-attribute-learning-using","title":"Efficient Relative Attribute Learning using Graph Neural Networks","date":"2018-09-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/generative-adversarial-network-with-spatial","slug":"generative-adversarial-network-with-spatial","title":"Generative Adversarial Network with Spatial Attention for Face Attribute Editing","date":"2018-09-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/few-shot-charge-prediction-with","slug":"few-shot-charge-prediction-with","title":"Few-Shot Charge Prediction with Discriminative Legal Attributes","date":"2018-08-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/improving-the-annotation-of-deepfashion","slug":"improving-the-annotation-of-deepfashion","title":"Improving the Annotation of DeepFashion Images for Fine-grained Attribute Recognition","date":"2018-07-31","arxiv_id":"1807.11674","repositories_listed":1,"syntology":null},{"url":"/paper/from-volcano-to-toyshop-adaptive","slug":"from-volcano-to-toyshop-adaptive","title":"From Volcano to Toyshop: Adaptive Discriminative Region Discovery for Scene Recognition","date":"2018-07-23","arxiv_id":"1807.08624","repositories_listed":1,"syntology":null},{"url":"/paper/nonconvex-optimization-for-regression-with","slug":"nonconvex-optimization-for-regression-with","title":"Nonconvex Optimization for Regression with Fairness Constraints","date":"2018-07-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/probabilistic-and-or-attribute-grouping-for","slug":"probabilistic-and-or-attribute-grouping-for","title":"Probabilistic AND-OR Attribute Grouping for Zero-Shot Learning","date":"2018-06-07","arxiv_id":"1806.02664","repositories_listed":1,"syntology":{"n":1,"n_ran":0,"n_constructed":0,"n_ran_checked":0,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"0 ran · 1 unverified","sample_list":"/paper/probabilistic-and-or-attribute-grouping-for#ran","syntology_url":"https://syntology.ai/paper/1806.02664","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1806.02664"}},"official":null}},{"url":"/paper/guider-a-guided-separate-and-conquer-rule","slug":"guider-a-guided-separate-and-conquer-rule","title":"GuideR: a guided separate-and-conquer rule learning in classification, regression, and survival settings","date":"2018-06-05","arxiv_id":"1806.01579","repositories_listed":1,"syntology":null},{"url":"/paper/deep-imbalanced-learning-for-face-recognition","slug":"deep-imbalanced-learning-for-face-recognition","title":"Deep Imbalanced Learning for Face Recognition and Attribute Prediction","date":"2018-06-01","arxiv_id":"1806.00194","repositories_listed":1,"syntology":null},{"url":"/paper/hopf-higher-order-propagation-framework-for","slug":"hopf-higher-order-propagation-framework-for","title":"HOPF: Higher Order Propagation Framework for Deep Collective Classification","date":"2018-05-31","arxiv_id":"1805.12421","repositories_listed":1,"syntology":null},{"url":"/paper/dual-swap-disentangling","slug":"dual-swap-disentangling","title":"Dual Swap Disentangling","date":"2018-05-27","arxiv_id":"1805.10583","repositories_listed":1,"syntology":null},{"url":"/paper/attriguard-a-practical-defense-against","slug":"attriguard-a-practical-defense-against","title":"AttriGuard: A Practical Defense Against Attribute Inference Attacks via Adversarial Machine Learning","date":"2018-05-13","arxiv_id":"1805.04810","repositories_listed":1,"syntology":null},{"url":"/paper/analyzing-covariate-influence-on-gender-and","slug":"analyzing-covariate-influence-on-gender-and","title":"Analyzing Covariate Influence on Gender and Race Prediction from Near-Infrared Ocular Images","date":"2018-05-04","arxiv_id":"1805.01912","repositories_listed":1,"syntology":null},{"url":"/paper/imbalanced-deep-learning-by-minority-class","slug":"imbalanced-deep-learning-by-minority-class","title":"Imbalanced Deep Learning by Minority Class Incremental Rectification","date":"2018-04-28","arxiv_id":"1804.10851","repositories_listed":1,"syntology":null},{"url":"/paper/detection-tracking-for-efficient-person","slug":"detection-tracking-for-efficient-person","title":"Detection-Tracking for Efficient Person Analysis: The DetTA Pipeline","date":"2018-04-26","arxiv_id":"1804.10134","repositories_listed":1,"syntology":null},{"url":"/paper/neural-davidsonian-semantic-proto-role","slug":"neural-davidsonian-semantic-proto-role","title":"Neural-Davidsonian Semantic Proto-role Labeling","date":"2018-04-21","arxiv_id":"1804.07976","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":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) · 0 unverified","sample_list":"/paper/neural-davidsonian-semantic-proto-role#ran","syntology_url":"https://syntology.ai/paper/1804.07976","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1804.07976"}},"official":{"repos":["decomp-sem/neural-sprl"],"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/a-large-scale-attribute-dataset-for-zero-shot","slug":"a-large-scale-attribute-dataset-for-zero-shot","title":"A Large-scale Attribute Dataset for Zero-shot Learning","date":"2018-04-12","arxiv_id":"1804.04314","repositories_listed":1,"syntology":null},{"url":"/paper/reducing-over-confident-errors-outside-the","slug":"reducing-over-confident-errors-outside-the","title":"Improving Confidence Estimates for Unfamiliar Examples","date":"2018-04-09","arxiv_id":"1804.03166","repositories_listed":1,"syntology":null},{"url":"/paper/attributes-as-operators-factorizing-unseen","slug":"attributes-as-operators-factorizing-unseen","title":"Attributes as Operators: Factorizing Unseen Attribute-Object Compositions","date":"2018-03-27","arxiv_id":"1803.09851","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":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) · 1 unverified","sample_list":"/paper/attributes-as-operators-factorizing-unseen#ran","syntology_url":"https://syntology.ai/paper/1803.09851","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1803.09851"}},"official":{"repos":["Tushar-N/attributes-as-operators"],"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/probabilistic-video-generation-using-holistic","slug":"probabilistic-video-generation-using-holistic","title":"Probabilistic Video Generation using Holistic Attribute Control","date":"2018-03-21","arxiv_id":"1803.08085","repositories_listed":1,"syntology":null},{"url":"/paper/zero-shot-detection","slug":"zero-shot-detection","title":"Zero-Shot Detection","date":"2018-03-19","arxiv_id":"1803.07113","repositories_listed":1,"syntology":null},{"url":"/paper/rankme-reliable-human-ratings-for-natural","slug":"rankme-reliable-human-ratings-for-natural","title":"RankME: Reliable Human Ratings for Natural Language Generation","date":"2018-03-15","arxiv_id":"1803.05928","repositories_listed":1,"syntology":null},{"url":"/paper/joint-pixel-and-feature-level-domain","slug":"joint-pixel-and-feature-level-domain","title":"Gotta Adapt 'Em All: Joint Pixel and Feature-Level Domain Adaptation for Recognition in the Wild","date":"2018-02-28","arxiv_id":"1803.00068","repositories_listed":1,"syntology":null},{"url":"/paper/protecting-sensory-data-against-sensitive","slug":"protecting-sensory-data-against-sensitive","title":"Protecting Sensory Data against Sensitive Inferences","date":"2018-02-21","arxiv_id":"1802.07802","repositories_listed":1,"syntology":{"n":3,"n_ran":2,"n_constructed":0,"n_ran_checked":0,"n_instrument":2,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/protecting-sensory-data-against-sensitive#ran","syntology_url":"https://syntology.ai/paper/1802.07802","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1802.07802"}},"official":{"repos":["mmalekzadeh/motion-sense"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/edge-attention-based-multi-relational-graph","slug":"edge-attention-based-multi-relational-graph","title":"Edge Attention-based Multi-Relational Graph Convolutional Networks","date":"2018-02-14","arxiv_id":"1802.04944","repositories_listed":1,"syntology":null},{"url":"/paper/holoface-augmenting-human-to-human","slug":"holoface-augmenting-human-to-human","title":"HoloFace: Augmenting Human-to-Human Interactions on HoloLens","date":"2018-02-01","arxiv_id":"1802.00278","repositories_listed":1,"syntology":null},{"url":"/paper/sentiment-analysis-by-capsules","slug":"sentiment-analysis-by-capsules","title":"Sentiment Analysis by Capsules","date":"2018-02-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/ptb-tir-a-thermal-infrared-pedestrian","slug":"ptb-tir-a-thermal-infrared-pedestrian","title":"PTB-TIR: A Thermal Infrared Pedestrian Tracking Benchmark","date":"2018-01-18","arxiv_id":"1801.05944","repositories_listed":1,"syntology":null},{"url":"/paper/face-synthesis-from-visual-attributes-via","slug":"face-synthesis-from-visual-attributes-via","title":"Face Synthesis from Visual Attributes via Sketch using Conditional VAEs and GANs","date":"2017-12-30","arxiv_id":"1801.00077","repositories_listed":1,"syntology":null},{"url":"/paper/class-rectification-hard-mining-for","slug":"class-rectification-hard-mining-for","title":"Class Rectification Hard Mining for Imbalanced Deep Learning","date":"2017-12-08","arxiv_id":"1712.03162","repositories_listed":1,"syntology":null},{"url":"/paper/inclusivefacenet-improving-face-attribute","slug":"inclusivefacenet-improving-face-attribute","title":"InclusiveFaceNet: Improving Face Attribute Detection with Race and Gender Diversity","date":"2017-12-01","arxiv_id":"1712.00193","repositories_listed":1,"syntology":null},{"url":"/paper/multimodal-attribute-extraction","slug":"multimodal-attribute-extraction","title":"Multimodal Attribute Extraction","date":"2017-11-29","arxiv_id":"1711.11118","repositories_listed":1,"syntology":null},{"url":"/paper/dna-gan-learning-disentangled-representations","slug":"dna-gan-learning-disentangled-representations","title":"DNA-GAN: Learning Disentangled Representations from Multi-Attribute Images","date":"2017-11-15","arxiv_id":"1711.05415","repositories_listed":1,"syntology":null},{"url":"/paper/adversarial-information-factorization","slug":"adversarial-information-factorization","title":"Adversarial Information Factorization","date":"2017-11-14","arxiv_id":"1711.05175","repositories_listed":1,"syntology":null},{"url":"/paper/the-un-reliability-of-saliency-methods","slug":"the-un-reliability-of-saliency-methods","title":"The (Un)reliability of saliency methods","date":"2017-11-02","arxiv_id":"1711.00867","repositories_listed":1,"syntology":null},{"url":"/paper/multiwinner-voting-with-fairness-constraints","slug":"multiwinner-voting-with-fairness-constraints","title":"Multiwinner Voting with Fairness Constraints","date":"2017-10-27","arxiv_id":"1710.10057","repositories_listed":1,"syntology":null},{"url":"/paper/on-the-long-term-memory-of-deep-recurrent","slug":"on-the-long-term-memory-of-deep-recurrent","title":"On the Long-Term Memory of Deep Recurrent Networks","date":"2017-10-25","arxiv_id":"1710.09431","repositories_listed":1,"syntology":null},{"url":"/paper/triangle-generative-adversarial-networks","slug":"triangle-generative-adversarial-networks","title":"Triangle Generative Adversarial Networks","date":"2017-09-19","arxiv_id":"1709.06548","repositories_listed":1,"syntology":null},{"url":"/paper/privacy-risk-in-machine-learning-analyzing","slug":"privacy-risk-in-machine-learning-analyzing","title":"Privacy Risk in Machine Learning: Analyzing the Connection to Overfitting","date":"2017-09-05","arxiv_id":"1709.01604","repositories_listed":1,"syntology":null},{"url":"/paper/cross-age-lfw-a-database-for-studying-cross","slug":"cross-age-lfw-a-database-for-studying-cross","title":"Cross-Age LFW: A Database for Studying Cross-Age Face Recognition in Unconstrained Environments","date":"2017-08-28","arxiv_id":"1708.08197","repositories_listed":1,"syntology":null},{"url":"/paper/cross-lingual-entity-alignment-via-joint","slug":"cross-lingual-entity-alignment-via-joint","title":"Cross-lingual Entity Alignment via Joint Attribute-Preserving Embedding","date":"2017-08-16","arxiv_id":"1708.05045","repositories_listed":1,"syntology":{"n":6,"n_ran":3,"n_constructed":0,"n_ran_checked":0,"n_instrument":3,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 3 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/cross-lingual-entity-alignment-via-joint#ran","syntology_url":"https://syntology.ai/paper/1708.05045","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1708.05045"}},"official":{"repos":["nju-websoft/JAPE"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/learning-a-repression-network-for-precise","slug":"learning-a-repression-network-for-precise","title":"Learning a Repression Network for Precise Vehicle Search","date":"2017-08-08","arxiv_id":"1708.02386","repositories_listed":1,"syntology":null},{"url":"/paper/automatic-spatially-aware-fashion-concept","slug":"automatic-spatially-aware-fashion-concept","title":"Automatic Spatially-aware Fashion Concept Discovery","date":"2017-08-03","arxiv_id":"1708.01311","repositories_listed":1,"syntology":null},{"url":"/paper/ulisboa-at-semeval-2017-task-12-extraction","slug":"ulisboa-at-semeval-2017-task-12-extraction","title":"ULISBOA at SemEval-2017 Task 12: Extraction and classification of temporal expressions and events","date":"2017-08-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/latent-relational-metric-learning-via-memory","slug":"latent-relational-metric-learning-via-memory","title":"Latent Relational Metric Learning via Memory-based Attention for Collaborative Ranking","date":"2017-07-17","arxiv_id":"1707.05176","repositories_listed":1,"syntology":null},{"url":"/paper/aga-attribute-guided-augmentation-1","slug":"aga-attribute-guided-augmentation-1","title":"AGA: Attribute-Guided Augmentation","date":"2017-07-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/imputets-time-series-missing-value-imputation","slug":"imputets-time-series-missing-value-imputation","title":"imputeTS: Time Series Missing Value Imputation in R","date":"2017-06-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/attributes2classname-a-discriminative-model","slug":"attributes2classname-a-discriminative-model","title":"Attributes2Classname: A discriminative model for attribute-based unsupervised zero-shot learning","date":"2017-05-04","arxiv_id":"1705.01734","repositories_listed":1,"syntology":null},{"url":"/paper/a-domain-based-approach-to-social-relation","slug":"a-domain-based-approach-to-social-relation","title":"A Domain Based Approach to Social Relation Recognition","date":"2017-04-21","arxiv_id":"1704.06456","repositories_listed":1,"syntology":null},{"url":"/paper/deep-variation-structured-reinforcement","slug":"deep-variation-structured-reinforcement","title":"Deep Variation-structured Reinforcement Learning for Visual Relationship and Attribute Detection","date":"2017-03-08","arxiv_id":"1703.03054","repositories_listed":1,"syntology":null},{"url":"/paper/on-the-expressive-power-of-overlapping","slug":"on-the-expressive-power-of-overlapping","title":"On the Expressive Power of Overlapping Architectures of Deep Learning","date":"2017-03-06","arxiv_id":"1703.02065","repositories_listed":1,"syntology":null},{"url":"/paper/matching-web-tables-to-dbpedia-a-feature","slug":"matching-web-tables-to-dbpedia-a-feature","title":"Matching web tables to DBpedia-A feature utility study","date":"2017-03-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/person-search-with-natural-language","slug":"person-search-with-natural-language","title":"Person Search with Natural Language Description","date":"2017-02-19","arxiv_id":"1702.05729","repositories_listed":1,"syntology":null},{"url":"/paper/on-the-effectiveness-of-discretizing","slug":"on-the-effectiveness-of-discretizing","title":"On the Effectiveness of Discretizing Quantitative Attributes in Linear Classifiers","date":"2017-01-24","arxiv_id":"1701.07114","repositories_listed":1,"syntology":null},{"url":"/paper/outlier-detection-for-text-data-an-extended","slug":"outlier-detection-for-text-data-an-extended","title":"Outlier Detection for Text Data : An Extended Version","date":"2017-01-05","arxiv_id":"1701.01325","repositories_listed":1,"syntology":null},{"url":"/paper/finding-statistically-significant-attribute","slug":"finding-statistically-significant-attribute","title":"Finding Statistically Significant Attribute Interactions","date":"2016-12-22","arxiv_id":"1612.07597","repositories_listed":1,"syntology":null},{"url":"/paper/learning-residual-images-for-face-attribute","slug":"learning-residual-images-for-face-attribute","title":"Learning Residual Images for Face Attribute Manipulation","date":"2016-12-16","arxiv_id":"1612.05363","repositories_listed":1,"syntology":null},{"url":"/paper/real-time-esports-match-result-prediction","slug":"real-time-esports-match-result-prediction","title":"Real-time eSports Match Result Prediction","date":"2016-12-10","arxiv_id":"1701.03162","repositories_listed":1,"syntology":null}],"record_sha256":"9be9991b6b2550ec79fab3bd1aa499be31c1d400e76ef7e1ff3049fb4d37cfce","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}