{"id":"W4406982876","doi":"10.1109/tpami.2025.3536845","title":"Unraveling the Mysteries of Label Noise in Source-Free Domain Adaptation: Theory and Practice","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","topic":"Music and Audio Processing","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Vector Institute; Western University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Domain adaptation; Computer science; Noise (video); Artificial intelligence; Adaptation (eye); Domain (mathematical analysis); Domain theory; Speech recognition; Pattern recognition (psychology); Mathematics; Psychology; Neuroscience","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009989814,0.000124814,0.0002124041,0.0003367877,0.0001880531,0.0001220011,0.0003662437,0.00003799928,0.00001225829],"category_scores_gemma":[0.00004632019,0.00009008731,0.00006101087,0.00106217,0.0001309894,0.0002638992,0.00001499794,0.0001796759,8.609668e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001271301,"about_ca_system_score_gemma":0.00003138165,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004815623,"about_ca_topic_score_gemma":0.0009079593,"domain_scores_codex":[0.9988304,0.0002640385,0.0003301544,0.0002933261,0.0001590809,0.000123043],"domain_scores_gemma":[0.9984308,0.0009637579,0.0001233881,0.0003817404,0.00006999791,0.00003030863],"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":[0.00005919357,0.0001076316,0.0003339835,0.00005488987,0.000265765,0.000003702701,0.006915011,0.01825759,0.0004489259,0.004076125,0.000002614692,0.9694746],"study_design_scores_gemma":[0.0009216316,0.0002412405,0.00399236,0.0005764076,0.001484836,0.00005144668,0.01257844,0.8623801,0.08171989,0.03438463,0.0009373095,0.0007316797],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005484384,0.0005794105,0.9906731,0.002850401,0.00006980812,0.00007876461,0.000005567757,0.0000188209,0.0002397233],"genre_scores_gemma":[0.9924873,0.0003007987,0.005755886,0.001261308,0.000005816273,0.00001110189,4.428457e-7,0.000003655982,0.0001737035],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9870029,"threshold_uncertainty_score":0.3673654,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02147427131098842,"score_gpt":0.2820735605137532,"score_spread":0.2605992892027648,"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."}}