{"id":"W4404295096","doi":"10.1109/mmsp61759.2024.10743968","title":"KPCA-CAM: Visual Explainability of Deep Computer Vision Models Using Kernel PCA","year":2024,"lang":"en","type":"article","venue":"","topic":"Explainable Artificial Intelligence (XAI)","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Artificial intelligence; Computer science; Kernel (algebra); Kernel principal component analysis; Computer vision; Pattern recognition (psychology); Computer graphics (images); Kernel method; Support vector machine; 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":[0.0007743026,0.0002422205,0.0003001225,0.0002539753,0.0001462155,0.0003906216,0.0009158905,0.0001196368,0.00009121688],"category_scores_gemma":[0.00002948767,0.0002132057,0.0001727705,0.0009186585,0.0001401705,0.002251603,0.0007121774,0.0001970924,0.0001035083],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001717824,"about_ca_system_score_gemma":0.0001400152,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003789824,"about_ca_topic_score_gemma":0.00004577352,"domain_scores_codex":[0.9974394,0.0001349622,0.0006050344,0.000810269,0.0005195956,0.0004907061],"domain_scores_gemma":[0.9985052,0.0002735214,0.00007598477,0.0007369195,0.0002600056,0.0001483321],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003263244,0.000467719,0.0001894115,0.0003135534,0.00005784415,0.0001181295,0.004991558,0.1566932,0.01195566,0.5492761,0.0004577225,0.2754464],"study_design_scores_gemma":[0.00004246331,0.0001702574,0.00003969437,0.0000643778,0.000007148186,0.00002201837,0.0001443003,0.9058054,0.02493483,0.06836848,0.0001792998,0.0002217499],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1450694,0.0002092337,0.8517153,0.0002710393,0.0006949459,0.0002305977,9.624114e-7,0.0003600356,0.001448479],"genre_scores_gemma":[0.8627336,0.00000878506,0.1368634,0.0001413551,0.0001096311,0.000006814632,0.000001230259,0.00001859777,0.0001165157],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7491122,"threshold_uncertainty_score":0.8694274,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0422627731121628,"score_gpt":0.33080447690206,"score_spread":0.2885417037898972,"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."}}