{"meta":{"page":1,"per_page":50,"max_per_page":100,"total":9,"total_is_capped":false,"direct_labels_cover":0,"predictions_cover":9,"direct_label_status":"direct model label, unvalidated","prediction_status":"machine_predicted_unvalidated (Codex and Gemma teacher distillation)","score_status":"score_only:v0-immature-baseline (scores rank; they never assert a category)","snapshot":{"source":"OpenAlex, pinned release, all 482 partitions","release":"2026-06-24","frame_built":"2026-07-12","author_layer_release":"2026-06-26"},"query_hash":"99b01d623da9","filters":{"venue":"Computer Vision and Pattern Recognition"}},"results":[{"id":"W2969721664","doi":"","title":"ProcSy: Procedural Synthetic Dataset Generation Towards Influence Factor Studies Of Semantic Segmentation Networks","year":2019,"lang":"en","type":"article","venue":"Computer Vision and Pattern Recognition","topic":"Machine Learning and Data Classification","field":"Computer Science","cited_by":32,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Artificial intelligence; Segmentation; Factor (programming language); Natural language processing; Programming language","authors":[{"name":"Samin Khan","is_ca":true},{"name":"Buu Phan","is_ca":true},{"name":"Rick Salay","is_ca":true},{"name":"Krzysztof Czarnecki","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.05097151400720926,"gpt":0.3210912784910192,"spread":0.2701197644838099,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002134535,0.001101706,0.0006852369,0.001956803,0.0006287834,0.001398237,0.002441698,0.001640494,0.004041957],"category_scores_gemma":[0.01141111,0.0005622304,0.001272948,0.001517811,0.0009373905,0.001295722,0.001369467,0.001471625,0.001132113],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001167182,"about_ca_system_score_gemma":0.001283634,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007697767,"about_ca_topic_score_gemma":0.01168137,"domain_scores_codex":[0.9989845,0.0003754319,0.00004392692,0.0002764837,0.0002234913,0.00009607303],"domain_scores_gemma":[0.995541,0.00258928,0.0001801935,0.000863033,0.000686523,0.0001399126],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00111815,0.0005630303,0.007928068,0.0007738017,0.0004477197,0.0004852785,0.000618774,0.5818738,0.0290585,0.07289205,0.04960938,0.2546315],"study_design_scores_gemma":[0.00003222031,0.00006604863,0.0007498003,0.00001675394,0.00002042858,0.00007377559,0.00005738546,0.9742923,0.006582662,0.01388511,0.004212391,0.00001115709],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09286672,0.0002720064,0.8849042,0.0004434287,0.0001603073,0.000446343,0.007903025,0.008165587,0.004838451],"genre_scores_gemma":[0.4356519,0.0002007216,0.5282427,0.0002138844,0.00008712587,0.0008154827,0.0300178,0.002013292,0.00275707],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007697767,"threshold_uncertainty_score":0.01530594,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2971644068","doi":"","title":"GAN Data Augmentation Through Active Learning Inspired Sample Acquisition.","year":2019,"lang":"en","type":"article","venue":"Computer Vision and Pattern Recognition","topic":"Advanced Data Compression Techniques","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Sample (material); Physics","authors":[{"name":"Christopher Nielsen","is_ca":true},{"name":"M. Okoniewski","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.04842322416993738,"gpt":0.3216162387348098,"spread":0.2731930145648724,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006133459,0.0007182847,0.0005539797,0.0003802461,0.000139139,0.0005544389,0.001071259,0.0006347506,0.002908346],"category_scores_gemma":[0.002481218,0.0003125738,0.0004240529,0.0005651141,0.0004491979,0.001094986,0.0008532042,0.001489136,0.001349118],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002317359,"about_ca_system_score_gemma":0.000436594,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008853235,"about_ca_topic_score_gemma":0.002005654,"domain_scores_codex":[0.9996958,0.0000680473,0.0000117736,0.00007822029,0.0001184805,0.00002777278],"domain_scores_gemma":[0.9992554,0.000289213,0.00004953767,0.0001823197,0.0001938842,0.0000296524],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005789052,0.0002685551,0.001694691,0.0002640801,0.0001340454,0.0001857919,0.0001603383,0.1901023,0.05522882,0.01891498,0.02062401,0.7118434],"study_design_scores_gemma":[0.000008622883,0.0000591583,0.0002744647,0.00001173826,0.00001029925,0.00008378329,0.00001101065,0.9767799,0.01522572,0.004074904,0.003451735,0.000008629908],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01071879,0.0003004343,0.9848791,0.0001377754,0.000127669,0.00004707427,0.0002480684,0.001547512,0.001993612],"genre_scores_gemma":[0.4500871,0.0004522834,0.5372027,0.000407172,0.0001648855,0.0002328879,0.002204166,0.0003911075,0.008857695],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002908346,"threshold_uncertainty_score":0.009729385,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2971746767","doi":"","title":"Limitations and Biases in Facial Landmark Detection D An Empirical Study on Older Adults with Dementia","year":2019,"lang":"en","type":"article","venue":"Computer Vision and Pattern Recognition","topic":"Face recognition and analysis","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Northern British Columbia; University of Regina; University of Toronto; Toronto Rehabilitation Institute","funders":"","keywords":"Landmark; Dementia; Computer science; Artificial intelligence; Cognitive psychology; Psychology; Medicine","authors":[{"name":"Azin Asgarian","is_ca":true},{"name":"Shun Zhao","is_ca":true},{"name":"Ahmed Ashraf","is_ca":true},{"name":"Matthew Browne","is_ca":true},{"name":"Kenneth M. Prkachin","is_ca":true},{"name":"Alex Mihailidis","is_ca":true},{"name":"Thomas Hadjistavropoulos","is_ca":true},{"name":"Babak Taati","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.05466912814512115,"gpt":0.2922324370851029,"spread":0.2375633089399817,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008487053,0.0003590028,0.0004629547,0.00132236,0.0009856542,0.001300363,0.0007372445,0.000707252,0.00216587],"category_scores_gemma":[0.07175258,0.0003108019,0.0004496957,0.0009725967,0.001205468,0.0014563,0.001090322,0.0004125585,0.0003844883],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000621428,"about_ca_system_score_gemma":0.000513519,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01690661,"about_ca_topic_score_gemma":0.02079532,"domain_scores_codex":[0.9956735,0.001893553,0.0006824415,0.0006857022,0.0008217024,0.0002430563],"domain_scores_gemma":[0.9654363,0.02160761,0.004604219,0.003051552,0.004881186,0.0004190805],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002559124,0.0001514034,0.9778159,0.00009002993,0.00006357121,0.000180382,0.005887405,0.00008105248,0.0003038438,0.0002002087,0.0002632791,0.01470697],"study_design_scores_gemma":[0.00002840776,0.0003124175,0.9871373,0.0001136433,0.00009933956,0.001170349,0.008122145,0.0004801194,0.0006382841,0.0006711578,0.001209064,0.00001767571],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9981096,0.0003074543,0.0002869079,0.00008292787,0.000008663206,0.00001998779,0.00009518378,0.000001738235,0.001087477],"genre_scores_gemma":[0.9990724,0.0001415317,0.0002330039,0.0001028901,0.00000961082,0.00003314723,0.00009384618,0.000002984112,0.000310438],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01690661,"threshold_uncertainty_score":0.04488438,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2971668727","doi":"","title":"Deep Probabilistic Regression of Elements of SO(3) using Quaternion Averaging and Uncertainty Injection.","year":2019,"lang":"en","type":"article","venue":"Computer Vision and Pattern Recognition","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Toronto","funders":"","keywords":"Quaternion; Probabilistic logic; Statistics; Regression; Regression analysis; Artificial intelligence; Measurement uncertainty; Mathematics; Computer science","authors":[{"name":"Valentin Peretroukhin","is_ca":true},{"name":"Brandon Wagstaff","is_ca":true},{"name":"and Jonathan Kelly","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01887778893913278,"gpt":0.2692569197375203,"spread":0.2503791307983875,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000488547,0.0006005278,0.0004967251,0.0003734222,0.0001841925,0.0004875245,0.000714404,0.0005253587,0.002314874],"category_scores_gemma":[0.001846483,0.0004120615,0.0006215646,0.0006584348,0.0004899746,0.0009686318,0.0008626477,0.001102234,0.000634891],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000314518,"about_ca_system_score_gemma":0.0006311884,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004752102,"about_ca_topic_score_gemma":0.00798773,"domain_scores_codex":[0.9997808,0.00006291457,0.000009636602,0.00004860864,0.00006843526,0.00002956866],"domain_scores_gemma":[0.9996461,0.0001392353,0.00005484518,0.00005698016,0.00007337263,0.00002952711],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001315511,0.00005453883,0.001309596,0.00009039541,0.0001200551,0.00007853831,0.00008109384,0.7678135,0.01297989,0.05764639,0.00491274,0.1547817],"study_design_scores_gemma":[0.000001955381,0.000008018964,0.000117811,0.000002981261,0.000003089453,0.000006956925,0.000003433394,0.9918996,0.0008730309,0.006632691,0.0004465925,0.000003915567],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01117625,0.0001490926,0.9872159,0.00009856484,0.00004906144,0.000009764985,0.00007183717,0.0004935641,0.0007359936],"genre_scores_gemma":[0.6556736,0.0004292958,0.3371229,0.0001460494,0.0001042418,0.00007429966,0.0006677698,0.0003871759,0.005394547],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004752102,"threshold_uncertainty_score":0.009448886,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2971967504","doi":"","title":"Application of DenseNet in Camera Model Identification and Post-processing Detection","year":2019,"lang":"en","type":"article","venue":"Computer Vision and Pattern Recognition","topic":"Image Processing and 3D Reconstruction","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Ottawa; University of Windsor","funders":"","keywords":"Computer science; Identification (biology); Computer vision; Artificial intelligence; Computer graphics (images)","authors":[{"name":"Abdul Muntakim Rafi","is_ca":true},{"name":"Uday Kamal","is_ca":false},{"name":"Md. Rakibul Hoque","is_ca":false},{"name":"Abid Abrar","is_ca":false},{"name":"Sowmitra Das","is_ca":false},{"name":"Robert Laganière","is_ca":true},{"name":"K. M. Azharul Hasan","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01042703376764862,"gpt":0.2380472585473151,"spread":0.2276202247796665,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006233363,0.0009247568,0.000732643,0.00170108,0.0005536241,0.0008980779,0.0008735445,0.00075414,0.002565894],"category_scores_gemma":[0.001405787,0.0005364415,0.0004764766,0.001356216,0.000458148,0.00126667,0.001119946,0.0004152523,0.001077314],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005267353,"about_ca_system_score_gemma":0.001024515,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008523556,"about_ca_topic_score_gemma":0.01032004,"domain_scores_codex":[0.9995263,0.00008092247,0.00002244955,0.0001307426,0.0001778497,0.00006177642],"domain_scores_gemma":[0.9994314,0.0001513746,0.00004899818,0.0001469827,0.0001989696,0.00002227448],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002095217,0.0001782826,0.004112333,0.0001941203,0.0001169456,0.0002652121,0.0001220256,0.1381629,0.04275294,0.0107751,0.004050138,0.7990605],"study_design_scores_gemma":[0.000007056922,0.00005569502,0.001537934,0.00001206772,0.00002508237,0.0002466687,0.00004055448,0.9685618,0.01946705,0.006306636,0.003727063,0.00001243294],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02068156,0.0003311884,0.975316,0.00009243347,0.00005842156,0.00004179702,0.0001799772,0.001537569,0.001760997],"genre_scores_gemma":[0.4190116,0.0005765984,0.5735148,0.0001579941,0.00007845298,0.0000835271,0.0011835,0.000213056,0.005180438],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008523556,"threshold_uncertainty_score":0.01694787,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2969186079","doi":"","title":"Generalized Zero-Shot Learning via Aligned Variational Autoencoders","year":2019,"lang":"en","type":"article","venue":"Computer Vision and Pattern Recognition","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Toronto","funders":"","keywords":"Shot (pellet); Zero (linguistics); Artificial intelligence; Computer science; One shot; Mathematics; Materials science; Engineering","authors":[{"name":"Edgar Schönfeld","is_ca":false},{"name":"Sayna Ebrahimi","is_ca":false},{"name":"Samarth Sinha","is_ca":true},{"name":"Trevor Darrell","is_ca":false},{"name":"Zeynep Akata","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02538265871613206,"gpt":0.2555607806244354,"spread":0.2301781219083034,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001412826,0.0009343076,0.001917338,0.0006407697,0.000447215,0.001101014,0.0027508,0.00206292,0.002217288],"category_scores_gemma":[0.004418506,0.0009836508,0.001117913,0.0006984024,0.001710276,0.002507348,0.002591757,0.002013041,0.0005243581],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007722379,"about_ca_system_score_gemma":0.00133681,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004584549,"about_ca_topic_score_gemma":0.005780159,"domain_scores_codex":[0.9993356,0.0002183084,0.00003352901,0.0001977782,0.0001325473,0.00008228151],"domain_scores_gemma":[0.9983543,0.001007846,0.000112955,0.0002111104,0.0002190274,0.00009480846],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002764303,0.0001678394,0.0008563204,0.0002543205,0.0002633162,0.0001111689,0.0001874164,0.7761289,0.008358977,0.06654385,0.003290899,0.1435606],"study_design_scores_gemma":[0.000005943115,0.00001762728,0.0000779971,0.000004666847,0.000006548322,0.00001393812,0.000006168157,0.984835,0.0004799274,0.01435955,0.0001848287,0.000007820352],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01156798,0.0002842056,0.9871992,0.000114532,0.00003600198,0.0000184434,0.00004726804,0.0001771305,0.0005552119],"genre_scores_gemma":[0.6484911,0.0006631939,0.3391443,0.0004295047,0.0001918683,0.0002145532,0.0008229202,0.0003498782,0.009692815],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004584549,"threshold_uncertainty_score":0.009115756,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2966777314","doi":"","title":"SANE: Towards Improved Prediction Robustness via Stochastically Activated Network Ensembles","year":2019,"lang":"en","type":"article","venue":"Computer Vision and Pattern Recognition","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Waterloo","funders":"","keywords":"Robustness (evolution); Computer science; Artificial intelligence","authors":[{"name":"Ibrahim Ben Daya","is_ca":true},{"name":"Mohammad Javad Shafiee","is_ca":true},{"name":"Michelle Karg","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01189665059216931,"gpt":0.2396290639766663,"spread":0.227732413384497,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003014118,0.001485341,0.001681695,0.0006520122,0.0005947661,0.001022571,0.002053939,0.002008351,0.002427246],"category_scores_gemma":[0.008808861,0.000790989,0.0009321678,0.0004701526,0.001292981,0.002260817,0.003502851,0.003531945,0.0007258686],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006031319,"about_ca_system_score_gemma":0.0008610775,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001726632,"about_ca_topic_score_gemma":0.002379474,"domain_scores_codex":[0.9987832,0.0004773205,0.00004929212,0.0002801359,0.0002963408,0.0001136839],"domain_scores_gemma":[0.9964709,0.002173177,0.0001923041,0.000580073,0.000443659,0.0001398515],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001663103,0.00006419005,0.0004159394,0.00004499069,0.0001202043,0.00005234324,0.00004155256,0.9348456,0.00311247,0.01321393,0.001818456,0.04610394],"study_design_scores_gemma":[0.000002701858,0.00001206515,0.00002212279,0.000001970078,0.000003752203,0.000004555771,0.000001276948,0.9961076,0.00040669,0.003349259,0.00008565145,0.000002273635],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02118557,0.0003337785,0.9753742,0.000294645,0.0001069565,0.00002919262,0.00008201395,0.0008377326,0.001755926],"genre_scores_gemma":[0.7918605,0.0003055944,0.2004012,0.0004870103,0.0002225054,0.0001533267,0.0004407608,0.0003755891,0.005753461],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003014118,"threshold_uncertainty_score":0.01594037,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2982305100","doi":"","title":"Discriminative Quantization for Fast Similarity Search.","year":2019,"lang":"en","type":"article","venue":"Computer Vision and Pattern Recognition","topic":"Advanced Image and Video Retrieval Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Waterloo","funders":"","keywords":"Discriminative model; Computer science; Artificial intelligence; Quantization (signal processing); Nearest neighbor search; Pattern recognition (psychology); Similarity (geometry); Algorithm","authors":[{"name":"Sepehr Eghbali","is_ca":true},{"name":"Ladan Tahvildari","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.03834193686331022,"gpt":0.3198599817939886,"spread":0.2815180449306784,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009465723,0.0005847243,0.001230716,0.00127753,0.0004323163,0.0009532016,0.001782722,0.0008892115,0.008965197],"category_scores_gemma":[0.006144609,0.0004021096,0.0003296992,0.002715444,0.0006825497,0.002307289,0.001464149,0.00133765,0.003605194],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006345562,"about_ca_system_score_gemma":0.001092472,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003877479,"about_ca_topic_score_gemma":0.007390764,"domain_scores_codex":[0.998957,0.0002818753,0.00007959898,0.0001677603,0.0003943112,0.0001194727],"domain_scores_gemma":[0.9987568,0.0004695208,0.00008093037,0.0003353514,0.0002922453,0.00006506766],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005657442,0.0001623019,0.0005981424,0.0004021057,0.00006320569,0.0001100227,0.000120618,0.03168918,0.02340194,0.07973878,0.04745819,0.8156898],"study_design_scores_gemma":[0.0001545657,0.0003013024,0.001080273,0.00006887253,0.00003901468,0.0004890223,0.0001107664,0.8578441,0.01867905,0.09919511,0.02199333,0.00004465826],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.008038514,0.003207483,0.9836469,0.0002385371,0.0002664459,0.00009609431,0.0003925356,0.001490736,0.002622706],"genre_scores_gemma":[0.3032287,0.001516953,0.6827947,0.0003506785,0.0003388843,0.0002676297,0.0026608,0.0002294596,0.008612207],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008965197,"threshold_uncertainty_score":0.02999157,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2971547406","doi":"","title":"SANE: Exploring Adversarial Robustness With Stochastically Activated Network Ensembles","year":2019,"lang":"en","type":"article","venue":"Computer Vision and Pattern Recognition","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Saskatchewan; University of Waterloo","funders":"","keywords":"Robustness (evolution); Adversarial system; Computer science; Artificial intelligence","authors":[{"name":"Ibrahim Ben Daya","is_ca":true},{"name":"Mohammad Javad Shafiee","is_ca":true},{"name":"Michelle Karg","is_ca":false},{"name":"Christian Scharfenberger","is_ca":true},{"name":"Alexander Wong","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02462234266608005,"gpt":0.2352726368110287,"spread":0.2106502941449487,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002709546,0.001237244,0.001235972,0.0007941717,0.0005262252,0.001161015,0.002016761,0.001861594,0.003063216],"category_scores_gemma":[0.008876719,0.0007478651,0.001078787,0.000475188,0.001663844,0.00215724,0.003029426,0.002396325,0.0004234161],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007197994,"about_ca_system_score_gemma":0.0007110304,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002149733,"about_ca_topic_score_gemma":0.001928775,"domain_scores_codex":[0.9991502,0.000422082,0.00002251173,0.0001392758,0.0001931312,0.00007286732],"domain_scores_gemma":[0.9955683,0.003539021,0.0001955443,0.0003321201,0.000245935,0.0001190787],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003555622,0.0000208869,0.0001976182,0.00002426841,0.00004751451,0.00002435805,0.0000172659,0.9784872,0.0004686927,0.01356235,0.0004692439,0.00664515],"study_design_scores_gemma":[0.000002151676,0.000008599335,0.0000168003,0.000001838052,0.000002783973,0.000003596776,0.000001545407,0.9930703,0.0001191935,0.006698555,0.00007289623,0.000001710609],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02821846,0.0003532625,0.9663327,0.0003723246,0.00007771678,0.00004347439,0.00009643422,0.0005702887,0.003935242],"genre_scores_gemma":[0.8630857,0.0003229107,0.1298986,0.0003439901,0.0001507597,0.000188842,0.0003072446,0.0003686465,0.005333413],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003063216,"threshold_uncertainty_score":0.01432967,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null}]}