{"id":"W4285816564","doi":"10.1109/i2ct54291.2022.9825204","title":"Quantitative Analysis of Transfer Learning for Image Classification","year":2022,"lang":"en","type":"article","venue":"2022 IEEE 7th International conference for Convergence in Technology (I2CT)","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"","keywords":"Transfer of learning; Computer science; Artificial intelligence; Machine learning; Task (project management); Inductive transfer; Image (mathematics); Contextual image classification; Robot learning; Engineering","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":[0.009330191,0.0007478095,0.0007256797,0.002759354,0.0005859944,0.001683108,0.001631984,0.001545926,0.004162703],"category_scores_gemma":[0.04637378,0.0002641678,0.0007122407,0.001702641,0.002541664,0.005169791,0.001907595,0.001929096,0.0006053587],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002255378,"about_ca_system_score_gemma":0.0008023389,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0010002,"about_ca_topic_score_gemma":0.0003731745,"domain_scores_codex":[0.9965055,0.001272663,0.0001246332,0.0003991107,0.001499894,0.0001982683],"domain_scores_gemma":[0.9744246,0.01833197,0.001374351,0.002278953,0.003218256,0.0003719986],"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":[0.0002907985,0.000191087,0.004181757,0.0008046727,0.0001722211,0.0001825924,0.0003984114,0.4536096,0.01384853,0.2240084,0.003213136,0.2990987],"study_design_scores_gemma":[0.000006375088,0.000118459,0.001864753,0.00003778187,0.00001841724,0.00009791342,0.00006028406,0.8849438,0.004935402,0.1064062,0.001487738,0.00002273239],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0286824,0.001810583,0.9619505,0.000786701,0.00008487263,0.0001073255,0.000113618,0.0005229016,0.005941092],"genre_scores_gemma":[0.8302217,0.001213789,0.1625474,0.0002978354,0.0003055737,0.0002802278,0.0003428587,0.0002695521,0.004521011],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009330191,"threshold_uncertainty_score":0.04934341,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06294828939284272,"score_gpt":0.3358222071443129,"score_spread":0.2728739177514702,"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."}}