{"id":"W4288375829","doi":"","title":"On Direct Distribution Matching for Adapting Segmentation Networks","year":2020,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Segmentation; Discriminator; Computer science; Artificial intelligence; Matching (statistics); Adversarial system; Context (archaeology); Stability (learning theory); Pattern recognition (psychology); Kernel (algebra); Scale-space segmentation; Image segmentation; Minification; Computer vision; Machine learning; Mathematics; Geography","routes":{"ca_aff":true,"ca_fund":false,"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":[0.002079198,0.001278681,0.00230033,0.001659504,0.000776505,0.001168579,0.003056751,0.00273883,0.004982293],"category_scores_gemma":[0.009304451,0.001009232,0.001003993,0.001856713,0.001406586,0.00299157,0.003026874,0.002123433,0.001585048],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001242906,"about_ca_system_score_gemma":0.001056398,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009483939,"about_ca_topic_score_gemma":0.008528684,"domain_scores_codex":[0.998943,0.0003277054,0.00004615619,0.0003785185,0.000188883,0.0001156648],"domain_scores_gemma":[0.9959454,0.002714655,0.0001593557,0.0006264223,0.0003878688,0.000166304],"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":[0.0003024377,0.0001599576,0.0007724343,0.0001224532,0.0001044045,0.0001066135,0.0001343561,0.6003367,0.009458371,0.01476119,0.004711773,0.3690293],"study_design_scores_gemma":[0.000008055601,0.00001570594,0.0001090332,0.000004575839,0.000006623652,0.0000227219,0.000009374579,0.9894004,0.000856315,0.009133134,0.0004291116,0.000004822703],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01199482,0.0004230522,0.9850684,0.0001840383,0.00005368159,0.00005460563,0.00008559286,0.0008678369,0.001267963],"genre_scores_gemma":[0.466138,0.001106662,0.5137547,0.0008936405,0.00045583,0.0002861538,0.001432113,0.001191778,0.01474116],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.009483939,"threshold_uncertainty_score":0.01885748,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02183591015221473,"score_gpt":0.246523171576899,"score_spread":0.2246872614246842,"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."}}