{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001656748,0.0001787482,0.000276964,0.0002184896,0.0001630612,0.00014025,0.000509512,0.0001415617,0.0000385826],"category_scores_gemma":[0.0001238011,0.0002214991,0.0001073215,0.0005528909,0.0001307543,0.0003539525,0.00126773,0.0005216632,0.000004501024],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004052667,"about_ca_system_score_gemma":0.0002005027,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002425257,"about_ca_topic_score_gemma":0.00004884856,"domain_scores_codex":[0.9985775,0.0002341546,0.0002078701,0.0007000672,0.00009731817,0.0001831091],"domain_scores_gemma":[0.9988082,0.0001678485,0.0002298837,0.0004421716,0.0002392029,0.0001126759],"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":[0.00003329829,0.0004047163,0.0353351,0.0002530286,0.0002424472,0.0004100879,0.008163796,0.719547,0.0008083091,0.2292522,0.00004006301,0.005509906],"study_design_scores_gemma":[0.0005279494,0.00006224443,0.01997778,0.00008043065,0.00005134355,0.000006622753,0.004893153,0.9703858,0.0002011577,0.003199839,0.0002944668,0.0003191807],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5235325,0.0001091845,0.475167,0.0000722944,0.0001400674,0.00007796929,0.000002548203,0.00009566599,0.0008028009],"genre_scores_gemma":[0.9928052,0.0002933526,0.005259674,0.00003985634,0.00002712891,3.894726e-7,0.00006478465,0.00001131094,0.001498326],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4699074,"threshold_uncertainty_score":0.9032472,"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."}}