{"id":"W2613498939","doi":"10.1109/tpami.2018.2884462","title":"Incremental Learning Through Deep Adaptation","year":2018,"lang":"en","type":"preprint","venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"Air Force Office of Scientific Research; Canada Research Chairs","keywords":"Computer science; Artificial intelligence; Adaptation (eye); Quantization (signal processing); Task (project management); Network architecture; Artificial neural network; Machine learning; Domain adaptation; Range (aeronautics); Domain (mathematical analysis); Network performance; Deep neural networks; Algorithm; Classifier (UML); Mathematics; Engineering","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004668083,0.0004388161,0.0005143191,0.0006470277,0.000483443,0.0004993733,0.0007427073,0.0002112228,0.0004381303],"category_scores_gemma":[0.00001297923,0.0004313094,0.0004295119,0.0009127298,0.0001216116,0.0003610521,0.00004407699,0.001132862,0.0001271837],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000942265,"about_ca_system_score_gemma":0.00005106922,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002263239,"about_ca_topic_score_gemma":0.001501851,"domain_scores_codex":[0.9970865,0.0003117129,0.0006355388,0.001070147,0.0005554653,0.0003406253],"domain_scores_gemma":[0.9984826,0.0001717188,0.0003740595,0.0006579566,0.000165498,0.0001482039],"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":[0.00001147234,0.00009533777,0.0001742964,0.00002878265,0.0006605225,0.000008054159,0.003639759,0.3658446,0.00004358209,0.0001090636,0.000004638174,0.6293799],"study_design_scores_gemma":[0.0001067473,0.0001608075,0.0003878433,0.00006285538,0.0005148913,0.000007780147,0.0004633508,0.9878358,0.008776241,0.0008979216,0.0002901421,0.0004956513],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001815095,0.0001995508,0.9960678,0.0002736269,0.0005807912,0.0002063727,0.00001700276,0.0002382735,0.0006015365],"genre_scores_gemma":[0.9786348,0.0007035692,0.01962212,0.0005243892,0.00006188609,0.00004599618,0.00003461983,0.00002551476,0.000347077],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9768198,"threshold_uncertainty_score":0.9998139,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03901779770268217,"score_gpt":0.2917025047683656,"score_spread":0.2526847070656834,"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."}}