{"id":"W2798956329","doi":"10.1109/cvpr.2018.00140","title":"HashGAN: Deep Learning to Hash with Pair Conditional Wasserstein GAN","year":2018,"lang":"en","type":"article","venue":"","topic":"Advanced Image and Video Retrieval Techniques","field":"Computer Science","cited_by":122,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Canadian Institute for Advanced Research","keywords":"Hash function; Computer science; Feature hashing; Artificial intelligence; Deep learning; Double hashing; Pairwise comparison; Overfitting; Pattern recognition (psychology); Similarity (geometry); Image retrieval; Hash table; Image (mathematics); Artificial neural network","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007131644,0.001072667,0.000848086,0.0004069878,0.0002155177,0.000582446,0.001583224,0.0009457745,0.002831855],"category_scores_gemma":[0.002247384,0.0003536112,0.0005059179,0.0004396843,0.0007981064,0.00162419,0.001399753,0.001835926,0.001304148],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008086709,"about_ca_system_score_gemma":0.0006564194,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002927092,"about_ca_topic_score_gemma":0.004635024,"domain_scores_codex":[0.999718,0.00007506237,0.00001077295,0.0000740287,0.00008215028,0.00003999018],"domain_scores_gemma":[0.9996001,0.0001392112,0.00003213437,0.0001336083,0.00006436095,0.00003052902],"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":[0.0005270267,0.000240991,0.002237274,0.0002175447,0.0002113226,0.0001655616,0.00009744718,0.4336969,0.01833722,0.02131603,0.02322006,0.4997325],"study_design_scores_gemma":[0.00002376859,0.00007532564,0.0001510645,0.000007180699,0.00001157682,0.00004117809,0.000008109019,0.9870749,0.003776263,0.007829412,0.0009920674,0.000009118989],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04938697,0.0008674386,0.9370362,0.0003970421,0.0001180567,0.0001318472,0.0005077008,0.008104361,0.003450388],"genre_scores_gemma":[0.668892,0.0005731365,0.3139546,0.0008515786,0.0001124625,0.0002540759,0.003070903,0.0006099325,0.01168136],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002927092,"threshold_uncertainty_score":0.009473443,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01091868193366021,"score_gpt":0.266492953822883,"score_spread":0.2555742718892228,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}