{"id":"W4388405697","doi":"10.1155/2023/3044155","title":"A New Multinetwork Mean Distillation Loss Function for Open‐World Domain Incremental Object Detection","year":2023,"lang":"en","type":"article","venue":"International Journal of Intelligent Systems","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Science and Technology Program of Guizhou Province; Petroleum Technology Research Centre; Ministry of Education of the People's Republic of China; National Natural Science Foundation of China","keywords":"Pascal (unit); Computer science; Distillation; Object detection; Artificial intelligence; Benchmark (surveying); Detector; Pattern recognition (psychology); Computer vision; Chromatography","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002200808,0.001799611,0.001322068,0.000933711,0.0005294519,0.001102437,0.003582927,0.002004207,0.002510721],"category_scores_gemma":[0.004021611,0.0005691349,0.00111105,0.0008705046,0.0009598054,0.003103523,0.002122348,0.003050673,0.001045503],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001796406,"about_ca_system_score_gemma":0.001943304,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006499465,"about_ca_topic_score_gemma":0.007648356,"domain_scores_codex":[0.9991148,0.0001636163,0.00005141049,0.0002490502,0.0003062061,0.0001148421],"domain_scores_gemma":[0.9990741,0.0003245642,0.00007897268,0.0001302533,0.0003258697,0.00006624656],"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":[0.0004875665,0.0003736523,0.001847794,0.0001911167,0.0002048104,0.0002392019,0.00007961672,0.4173985,0.01675722,0.009043452,0.01544886,0.5379283],"study_design_scores_gemma":[0.00001058132,0.00004708251,0.0002024079,0.000007606156,0.00001482058,0.00004370975,0.000004235127,0.9934999,0.003167995,0.00195229,0.001035069,0.00001428955],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03049835,0.001431872,0.9605358,0.0005951227,0.0002163849,0.0001109457,0.0003327456,0.004274942,0.00200388],"genre_scores_gemma":[0.52593,0.000863441,0.4524092,0.001285995,0.0002791743,0.0004361241,0.003017968,0.0008402505,0.01493782],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006499465,"threshold_uncertainty_score":0.01303393,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03736207295549617,"score_gpt":0.3231840955175753,"score_spread":0.2858220225620791,"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."}}