{"id":"W4312602363","doi":"10.1109/cvpr52688.2022.00129","title":"GANSeg: Learning to Segment by Unsupervised Hierarchical Image Generation","year":2022,"lang":"en","type":"article","venue":"2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","topic":"Generative Adversarial Networks and Image Synthesis","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Huawei Technologies; Compute Canada; Automotive Research Center","keywords":"Computer science; Artificial intelligence; Robustness (evolution); Segmentation; Code (set theory); Image (mathematics); Unsupervised learning; Computer vision; Pattern recognition (psychology); Image segmentation; Artificial neural network; Set (abstract data type)","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000687997,0.001591762,0.0008709672,0.0006203232,0.0002482017,0.0006059977,0.002274084,0.001291651,0.004320383],"category_scores_gemma":[0.001548285,0.0006754807,0.001069538,0.0004715862,0.0008139403,0.0009305036,0.00128888,0.001832556,0.002164429],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007238952,"about_ca_system_score_gemma":0.0006639861,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002894383,"about_ca_topic_score_gemma":0.00600917,"domain_scores_codex":[0.9996237,0.00007799849,0.00001255086,0.000154622,0.00008645198,0.00004476821],"domain_scores_gemma":[0.9995784,0.0001627627,0.00003689122,0.0001419962,0.00005177521,0.00002807058],"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":[0.0002690705,0.0001298906,0.000985065,0.0002169765,0.0001612297,0.0001723909,0.000110825,0.6343265,0.02866884,0.01246795,0.02093037,0.301561],"study_design_scores_gemma":[0.00001369388,0.00003661665,0.0001252196,0.000008561725,0.000009070718,0.00006187159,0.000006108825,0.9882152,0.004469221,0.00520309,0.001843154,0.000008126157],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01084006,0.0004570328,0.9750505,0.0001668586,0.00009310013,0.0001259246,0.0004340106,0.009924205,0.002908317],"genre_scores_gemma":[0.3090051,0.0004085352,0.6740012,0.0006751131,0.0001030232,0.0004193272,0.003981549,0.002384869,0.009021282],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004320383,"threshold_uncertainty_score":0.01445311,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03295431514156284,"score_gpt":0.2556871973389931,"score_spread":0.2227328821974303,"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."}}