{"id":"W4287777770","doi":"10.48550/arxiv.2005.07839","title":"Joint Progressive Knowledge Distillation and Unsupervised Domain\\n Adaptation","year":2020,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Genetec (Canada)","funders":"","keywords":"Computer science; Artificial intelligence; Domain adaptation; Domain (mathematical analysis); Divergence (linguistics); Machine learning; Set (abstract data type); Pattern recognition (psychology); Data mining; Classifier (UML)","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":[0.0008330728,0.001207021,0.0009640377,0.0005923505,0.0003407339,0.0008443628,0.001958185,0.001236168,0.002207445],"category_scores_gemma":[0.003168888,0.0004805232,0.0008653611,0.000812247,0.001253755,0.002331879,0.002683505,0.002106628,0.0008327327],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000689772,"about_ca_system_score_gemma":0.001365763,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00518642,"about_ca_topic_score_gemma":0.00810961,"domain_scores_codex":[0.9994911,0.0001124829,0.00002999768,0.0001967402,0.00009367525,0.00007597303],"domain_scores_gemma":[0.9988725,0.0004624587,0.00008868688,0.0003834488,0.0001447047,0.00004821403],"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":[0.0001562929,0.0002036224,0.001115157,0.0001535365,0.0001053071,0.0001714252,0.000190595,0.5647065,0.01137268,0.02057879,0.004497739,0.3967484],"study_design_scores_gemma":[0.000007891024,0.00002783177,0.000124241,0.000007477166,0.00001040427,0.00003489185,0.00001584587,0.9872126,0.00366923,0.007519013,0.001360995,0.000009548841],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02917047,0.0004981562,0.9643158,0.0002931701,0.00006632532,0.00007502081,0.0001493138,0.002145462,0.003286278],"genre_scores_gemma":[0.7318114,0.0004874914,0.2575106,0.0005876317,0.0001458433,0.0002207347,0.0009002603,0.0002901691,0.008045888],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00518642,"threshold_uncertainty_score":0.01031244,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1349601155401389,"score_gpt":0.204635095105935,"score_spread":0.0696749795657961,"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."}}