{"id":"W4402261906","doi":"10.1109/tip.2024.3451938","title":"Privacy-Preserving Autoencoder for Collaborative Object Detection","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Image Processing","topic":"Face recognition and analysis","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"Alliance de recherche numérique du Canada","keywords":"Computer science; Artificial intelligence; Autoencoder; Bottleneck; Information bottleneck method; Computer vision; Codec; Information privacy; Decoding methods; Feature extraction; Object detection; Cloud computing; Artificial neural network; Machine learning; Data mining; Pattern recognition (psychology); Cluster analysis; Computer security; Embedded system","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001588604,0.0005548788,0.0006755167,0.0003707362,0.0003946505,0.000853889,0.001411145,0.00100283,0.001585446],"category_scores_gemma":[0.004352895,0.0003396962,0.0005754207,0.0004168734,0.0009063921,0.001648455,0.001511466,0.001602382,0.0007627844],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008321619,"about_ca_system_score_gemma":0.001138086,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002343383,"about_ca_topic_score_gemma":0.002815751,"domain_scores_codex":[0.9987295,0.000256282,0.00005151855,0.000295387,0.0005343521,0.0001329131],"domain_scores_gemma":[0.9979712,0.000801228,0.0001929129,0.0006679402,0.0003033241,0.00006325946],"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":[0.0005606264,0.0002337064,0.001714467,0.0001294161,0.0001489349,0.0003132316,0.0001894231,0.5267154,0.06266748,0.03110717,0.004094715,0.3721254],"study_design_scores_gemma":[0.000005785278,0.00003254304,0.000223439,0.000006127897,0.000008909765,0.00007412858,0.000008715512,0.9748081,0.01853648,0.005494987,0.0007922446,0.000008490081],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01370369,0.0001162252,0.9842652,0.00009967917,0.00002332926,0.00002405631,0.0000558355,0.0007214179,0.0009904542],"genre_scores_gemma":[0.6525221,0.0002558728,0.34119,0.0002513534,0.00006404029,0.0001004321,0.0003399422,0.0001352867,0.005141045],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002343383,"threshold_uncertainty_score":0.008401394,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02053213404869664,"score_gpt":0.2858990334323385,"score_spread":0.2653668993836418,"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."}}