{"id":"W2894854504","doi":"10.3390/app8122512","title":"Transfer Incremental Learning Using Data Augmentation","year":2018,"lang":"en","type":"article","venue":"Applied Sciences","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Mila - Quebec Artificial Intelligence Institute","funders":"","keywords":"Computer science; Artificial intelligence; Machine learning; Flexibility (engineering); Transfer of learning; Feature selection; Incremental learning; Feature (linguistics); Deep learning; Class (philosophy); Selection (genetic algorithm); 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001070475,0.00008965645,0.00007912661,0.0001076501,0.0008565599,0.0003616755,0.001322083,0.0000257813,0.0000958381],"category_scores_gemma":[0.00002045819,0.00008200829,0.00001380248,0.0006756247,0.0003291069,0.001295079,0.0002832161,0.00009802994,0.0001289144],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002500642,"about_ca_system_score_gemma":0.00008619063,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003389114,"about_ca_topic_score_gemma":0.00001135143,"domain_scores_codex":[0.9985732,0.00005321598,0.0001662945,0.0005017859,0.0004511094,0.0002543682],"domain_scores_gemma":[0.9995162,0.00005650331,0.00004456128,0.0002936689,0.00002731231,0.00006167657],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000018655,0.00006583825,0.001704243,0.000009487805,0.00002227203,0.000003680116,0.007598689,0.004275138,0.4575714,0.3611292,0.0003423044,0.1672592],"study_design_scores_gemma":[0.0005658333,0.0001485144,0.000836983,0.000014068,0.000008960274,0.00001190705,0.002813705,0.9602913,0.0196289,0.001526123,0.01382571,0.0003279896],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1607932,0.00001722958,0.8169627,0.0001831863,0.0002293328,0.0001055201,8.892288e-7,0.0001391679,0.02156879],"genre_scores_gemma":[0.9091784,0.000002222306,0.09016554,0.0004878247,0.00009358916,0.000002519198,0.000007006599,0.000003980845,0.00005888785],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9560162,"threshold_uncertainty_score":0.6588053,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1335394557429295,"score_gpt":0.3380208516383365,"score_spread":0.204481395895407,"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."}}