{"id":"W3086607822","doi":"","title":"'Less Than One'-Shot Learning: Learning N Classes From M<N Samples","year":2020,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":27,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Artificial intelligence; Computer science; Classifier (UML); Machine learning; Generalization; One shot; Shot (pellet); Artificial neural network; Robustness (evolution); Class (philosophy); Task (project management); Training set; Single shot; Pattern recognition (psychology); Mathematics; Engineering","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":[0.004084519,0.001248517,0.002576858,0.0008871023,0.001298227,0.002221563,0.004334989,0.003318314,0.003266237],"category_scores_gemma":[0.02011769,0.0007984795,0.001148043,0.0007398422,0.002977141,0.006972434,0.004509011,0.003949288,0.0007052046],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002463954,"about_ca_system_score_gemma":0.001285863,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00511584,"about_ca_topic_score_gemma":0.004221748,"domain_scores_codex":[0.9977472,0.0006726262,0.0001287084,0.0008168813,0.0004050305,0.0002295045],"domain_scores_gemma":[0.991073,0.006201934,0.0004563685,0.001247769,0.0004773817,0.0005435109],"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":[0.001382698,0.0007644433,0.006284636,0.0006492692,0.0003035843,0.0003459425,0.0006379477,0.5121882,0.009722418,0.08746722,0.007971427,0.3722821],"study_design_scores_gemma":[0.00002688931,0.0001077236,0.0005966105,0.00002574031,0.00001807268,0.00008108077,0.00006290084,0.9257023,0.00299046,0.06977683,0.0005903121,0.00002102703],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.08866384,0.0007998793,0.9042112,0.001482666,0.00007814093,0.0002320315,0.0002030521,0.0008565573,0.003472611],"genre_scores_gemma":[0.7507359,0.000358785,0.2411467,0.0009848168,0.0001416288,0.0003450036,0.0009179089,0.0001885831,0.005180709],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00511584,"threshold_uncertainty_score":0.0216012,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2616300794238546,"score_gpt":0.2222294257833783,"score_spread":0.03940065364047632,"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."}}