{"id":"W4206056756","doi":"10.1007/978-3-030-90439-5_46","title":"Unsupervised Pixel-Wise Weighted Adversarial Domain Adaptation","year":2021,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Artificial intelligence; Segmentation; Leverage (statistics); Weighting; Pattern recognition (psychology); Domain adaptation; Pixel; Machine learning; Domain (mathematical analysis); Feature (linguistics); Convolutional neural network; Adaptation (eye); Classifier (UML); Mathematics","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":[0.0006973496,0.0009326347,0.001080493,0.0004743189,0.0002643417,0.0007580872,0.001822793,0.001174813,0.003536962],"category_scores_gemma":[0.001651411,0.0004866091,0.0009145165,0.0007682809,0.0007402265,0.001205635,0.002172415,0.002004788,0.002072364],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004130103,"about_ca_system_score_gemma":0.0004518043,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001508765,"about_ca_topic_score_gemma":0.002290273,"domain_scores_codex":[0.9996402,0.0000791667,0.00001184912,0.0001358695,0.0000916127,0.00004134872],"domain_scores_gemma":[0.9994196,0.0002621283,0.00003312,0.0001659628,0.00008741608,0.00003184645],"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":[0.0001549289,0.0001248814,0.0003602859,0.0001610088,0.0001401588,0.0001252119,0.00005529432,0.5495808,0.02710884,0.02130355,0.01127264,0.3896124],"study_design_scores_gemma":[0.000002887577,0.00001806852,0.0001119403,0.000007209452,0.000009394069,0.00006761763,0.000004944254,0.9865819,0.003670583,0.008054116,0.001464244,0.00000716136],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003674842,0.0002792705,0.9936212,0.00006307814,0.00005434239,0.000022405,0.00007817816,0.0005437387,0.001662986],"genre_scores_gemma":[0.3624987,0.001438485,0.5985196,0.0005292083,0.0002215121,0.0002191748,0.001913819,0.000811604,0.03384804],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003536962,"threshold_uncertainty_score":0.01183236,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01971505677163591,"score_gpt":0.2344875737945281,"score_spread":0.2147725170228922,"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."}}