{"id":"W3200580571","doi":"10.48550/arxiv.2109.05675","title":"Online Unsupervised Learning of Visual Representations and Categories","year":2021,"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":"University of Toronto","funders":"","keywords":"Categorical variable; Computer science; Artificial intelligence; Unsupervised learning; Representation (politics); Class (philosophy); Contrast (vision); Component (thermodynamics); Machine learning; Visual learning; Concept learning; Feature learning; Pattern recognition (psychology); 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007496446,0.0005380689,0.0007213885,0.0007981088,0.0003552613,0.0007881374,0.002499266,0.001133263,0.001656047],"category_scores_gemma":[0.004382757,0.0004275866,0.0007292778,0.0006248743,0.0009352705,0.00278206,0.001483537,0.001753845,0.0007608366],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000761733,"about_ca_system_score_gemma":0.0006412297,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002493289,"about_ca_topic_score_gemma":0.004143209,"domain_scores_codex":[0.9993865,0.0001320105,0.00001768263,0.0002842266,0.000110763,0.00006874534],"domain_scores_gemma":[0.9983661,0.0006271101,0.0001725459,0.0004769964,0.0002577713,0.00009944205],"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":[0.0003051009,0.000534244,0.005502,0.0002254157,0.0001669259,0.0001326867,0.0003017008,0.2526833,0.02861622,0.0165963,0.008215756,0.6867204],"study_design_scores_gemma":[0.000007489343,0.00003464792,0.0008351215,0.000007049752,0.000007914927,0.00005116597,0.00003030411,0.9783111,0.004913278,0.01516952,0.000623416,0.000008935917],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08974127,0.0003139753,0.9055189,0.0002811864,0.00005868562,0.00007271497,0.0002350574,0.001928997,0.00184911],"genre_scores_gemma":[0.8243309,0.00020399,0.1684853,0.0002561542,0.00008962296,0.0001340979,0.001110806,0.0001965434,0.005192666],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002499266,"threshold_uncertainty_score":0.005540073,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07116931112499322,"score_gpt":0.2255765375790393,"score_spread":0.1544072264540461,"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."}}