{"id":"W3216046900","doi":"10.48550/arxiv.2112.01036","title":"GANSeg: Learning to Segment by Unsupervised Hierarchical Image Generation","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Generative Adversarial Networks and Image Synthesis","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Compute Canada","keywords":"Artificial intelligence; Computer science; Robustness (evolution); Segmentation; Image (mathematics); Computer vision; Pattern recognition (psychology); Object (grammar); Unsupervised learning; Image segmentation","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003064329,0.0003869374,0.0003977498,0.0001726863,0.0003299595,0.0005686599,0.001203108,0.0002433578,0.0001421194],"category_scores_gemma":[0.00007538568,0.0004541318,0.0002421974,0.0006127435,0.00006463881,0.0004852313,0.002041369,0.0007225481,0.00007640607],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002319536,"about_ca_system_score_gemma":0.0002120888,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001268832,"about_ca_topic_score_gemma":0.0000352303,"domain_scores_codex":[0.9970357,0.0005560562,0.0002500125,0.001532664,0.0001656196,0.0004599469],"domain_scores_gemma":[0.9982616,0.00008429401,0.0001337686,0.0009343975,0.0002577824,0.0003282088],"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":[0.00001656158,0.0001210886,0.0002249349,0.00002490812,0.0001140943,0.0002560683,0.0007946016,0.9680409,0.02068096,0.001422828,0.004433108,0.003869988],"study_design_scores_gemma":[0.0003176123,0.00007596533,0.0001442449,0.000050209,0.00004515719,0.000002501156,0.0001330965,0.9883457,0.007801374,0.0003188298,0.002215127,0.0005501294],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1299753,0.00008503174,0.8677338,0.000453087,0.000563583,0.0002750555,0.000009399418,0.0001487523,0.000755981],"genre_scores_gemma":[0.9681568,0.0001366982,0.02933701,0.0005277228,0.0002691948,0.000003144086,0.00009411038,0.00002597124,0.001449324],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8383968,"threshold_uncertainty_score":0.999791,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04701729669888357,"score_gpt":0.1844635810616375,"score_spread":0.1374462843627539,"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."}}