{"id":"W2991989919","doi":"10.1109/tnnls.2019.2952322","title":"CHIP: Channel-Wise Disentangled Interpretation of Deep Convolutional Neural Networks","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks and Learning Systems","topic":"Explainable Artificial Intelligence (XAI)","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; University of British Columbia","keywords":"Computer science; Discriminative model; Artificial intelligence; Convolutional neural network; Interpretation (philosophy); Machine learning; Pattern recognition (psychology); Class (philosophy); Regularization (linguistics); Channel (broadcasting); Deep learning","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0003689165,0.0002495284,0.0003566405,0.0001651794,0.0003310246,0.0001944007,0.0003549827,0.0001419668,0.00002053836],"category_scores_gemma":[0.000008935248,0.0002352763,0.0001446002,0.0004776041,0.00008772934,0.000579873,0.000008705458,0.0006868598,0.00001408473],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005048138,"about_ca_system_score_gemma":0.00001248074,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001609596,"about_ca_topic_score_gemma":0.00003846188,"domain_scores_codex":[0.9978368,0.0004228556,0.0005158635,0.0004996375,0.0002955679,0.0004293137],"domain_scores_gemma":[0.9987147,0.0004349542,0.0002555085,0.0003260743,0.0001277161,0.0001409988],"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":[0.00007991949,0.00004992641,0.000303232,0.00003229473,0.00003266561,0.000005288804,0.0005045055,0.9817476,0.00009225316,0.0004715239,0.00001158137,0.01666917],"study_design_scores_gemma":[0.0002280578,0.0003796571,0.0002923603,0.00009176377,0.00001780124,0.00003994263,0.0003173255,0.9982568,0.00008923703,0.00002013182,0.00003958706,0.0002273364],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1263591,0.0005688046,0.8694546,0.0000961569,0.002871156,0.0004079202,0.000001412147,0.0001486893,0.00009211001],"genre_scores_gemma":[0.9992352,0.00004580446,0.00007286025,0.00008443138,0.0001539089,0.00004064533,0.000004732718,0.0000237731,0.0003387059],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.872876,"threshold_uncertainty_score":0.9594291,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01118582938150223,"score_gpt":0.2286773161765149,"score_spread":0.2174914867950127,"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."}}