{"id":"W4380740422","doi":"10.1117/12.2663916","title":"Normalized determinant pooling layer in CNNs for multi-label classification","year":2023,"lang":"en","type":"article","venue":"","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Alberta Oil Sands Technology and Research Authority","funders":"","keywords":"Convolutional neural network; Artificial intelligence; Pooling; Computer science; Pattern recognition (psychology); Pixel; Feature vector; Contextual image classification; Scaling; Feature (linguistics); Deep learning; Computer vision; Image (mathematics); 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":[0.0009134422,0.00008019552,0.0001183098,0.000191864,0.00008223273,0.0001064356,0.0003760958,0.00004921796,0.000003556391],"category_scores_gemma":[0.000133261,0.00006838121,0.00003928916,0.0005377003,0.00001182524,0.0003365108,0.00008959266,0.000061174,0.00007708083],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002458804,"about_ca_system_score_gemma":0.00003601726,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004203511,"about_ca_topic_score_gemma":0.00003142713,"domain_scores_codex":[0.999069,0.00007610686,0.0002121709,0.0002597196,0.0001105339,0.0002724157],"domain_scores_gemma":[0.9994019,0.0001949083,0.00004278216,0.0002613864,0.00006016983,0.00003891437],"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":[0.00003848774,0.0001262242,0.0008780634,0.0000442927,0.000008237223,0.00004899346,0.001479436,0.00009697134,0.3385066,0.02050058,0.001041105,0.637231],"study_design_scores_gemma":[0.001241478,0.00002531636,0.007555058,0.00001291604,0.000002042075,0.000003534645,0.00002693067,0.9663143,0.02311604,0.000919128,0.0006748436,0.0001083725],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0322137,0.00001533993,0.9662966,0.0003950533,0.0002459399,0.0001955478,8.326599e-7,0.0001967123,0.0004403303],"genre_scores_gemma":[0.1627419,0.000009995209,0.8335255,0.0003285542,0.00003389411,0.00005906156,0.000003620147,0.00000959861,0.003287903],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9662174,"threshold_uncertainty_score":0.2788505,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2220690810001456,"score_gpt":0.4060120269585833,"score_spread":0.1839429459584377,"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."}}