{"id":"W3207497018","doi":"10.1109/icra48506.2021.9561466","title":"LiDAR few-shot domain adaptation via integrated CycleGAN and 3D object detector with joint learning delay","year":2021,"lang":"en","type":"article","venue":"","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Huawei Technologies (Canada)","funders":"","keywords":"Computer science; Lidar; Artificial intelligence; Point cloud; Task (project management); Object detection; Minimum bounding box; Margin (machine learning); Domain (mathematical analysis); Detector; Joint (building); Bounding overwatch; Object (grammar); Network architecture; Adaptation (eye); Machine learning; Pattern recognition (psychology); Image (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":[0.0004416791,0.0002392709,0.0002490849,0.0001624891,0.0003437776,0.0005097509,0.000214795,0.0000877647,0.0001466932],"category_scores_gemma":[0.0001144407,0.0001973724,0.00006392049,0.0007853852,0.00006848819,0.0006169507,0.0001378315,0.000432278,0.00005053791],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000682824,"about_ca_system_score_gemma":0.0002098535,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009986139,"about_ca_topic_score_gemma":0.0003575875,"domain_scores_codex":[0.998008,0.0003278169,0.0003111436,0.0006063052,0.0003833981,0.0003633493],"domain_scores_gemma":[0.9990029,0.0001414319,0.0001444624,0.0003184719,0.0001982753,0.0001944683],"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":[0.0002949795,0.0003655808,0.009855237,0.0001753076,0.0005229905,0.001711851,0.04305563,0.03773538,0.1753328,0.04146022,0.0001673808,0.6893227],"study_design_scores_gemma":[0.002646292,0.0008136384,0.0150921,0.0001656274,0.00004212035,0.0008879946,0.009168822,0.9292082,0.01412499,0.0009931269,0.02572319,0.001133965],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1300882,0.0001689401,0.8626708,0.0003163148,0.0001105955,0.0001260733,4.757516e-7,0.0003627897,0.006155854],"genre_scores_gemma":[0.7232962,0.00001699407,0.2750582,0.0003502727,0.00002818706,0.00001145468,0.00001080423,0.00001996677,0.0012079],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8914728,"threshold_uncertainty_score":0.8048612,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01937140428939694,"score_gpt":0.2220868300012766,"score_spread":0.2027154257118797,"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."}}