{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007733653,0.0001573023,0.000329578,0.001757187,0.0002248331,0.00004548931,0.001468395,0.00009554937,0.0004688951],"category_scores_gemma":[0.0003410898,0.000186356,0.0001981842,0.002067532,0.0001613425,0.0003415314,0.0001342068,0.000395655,0.000004695081],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000162308,"about_ca_system_score_gemma":0.0001709537,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002531013,"about_ca_topic_score_gemma":0.00004212649,"domain_scores_codex":[0.9980801,0.000104059,0.000581165,0.0005733981,0.0003853112,0.0002759153],"domain_scores_gemma":[0.9986075,0.0003071711,0.0002650152,0.0002912827,0.0004929223,0.00003609404],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001389809,0.00009700642,0.007457633,0.0000178754,0.0003705166,0.000003771344,0.001955634,0.01685911,0.03468625,0.9319298,0.0001076647,0.006375798],"study_design_scores_gemma":[0.0005989611,0.000370503,0.002279066,0.000008344421,0.00005767525,0.000002781387,0.004230012,0.9755778,0.002925144,0.0100778,0.003666787,0.0002050687],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1642449,0.00002772532,0.8317617,0.002004447,0.0007387421,0.0004330886,0.00008949756,0.0001232053,0.0005767144],"genre_scores_gemma":[0.9743058,0.00002942849,0.02414233,0.00007859958,0.000009827372,0.0008683184,0.0001206447,0.00001186919,0.0004331826],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9587187,"threshold_uncertainty_score":0.7599376,"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."}}