{"id":"W4386243173","doi":"10.1109/crv60082.2023.00009","title":"LatentKeypointGAN: Controlling Images via Latent Keypoints","year":2023,"lang":"en","type":"article","venue":"","topic":"Generative Adversarial Networks and Image Synthesis","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Artificial intelligence; Computer vision; Image (mathematics); Set (abstract data type); Matching (statistics); Domain (mathematical analysis); Generative grammar; Generative model; Image translation; Pattern recognition (psychology); 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005643764,0.0009507231,0.0004194298,0.0002804702,0.0001669531,0.0006615253,0.001028313,0.000576022,0.003506638],"category_scores_gemma":[0.001945758,0.0003968577,0.0005608323,0.0002267089,0.0008521577,0.0009735029,0.001237113,0.001571242,0.0008432913],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004885747,"about_ca_system_score_gemma":0.0002875063,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001078539,"about_ca_topic_score_gemma":0.001874504,"domain_scores_codex":[0.999693,0.00007936115,0.000008333638,0.0001090408,0.00007789834,0.00003226363],"domain_scores_gemma":[0.9995849,0.0002147109,0.0000446655,0.00009481511,0.00003704549,0.00002387386],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000224379,0.00008474758,0.001048547,0.0001600978,0.00009852817,0.0001607455,0.0001499028,0.7471671,0.06074429,0.03831806,0.006260488,0.1455831],"study_design_scores_gemma":[0.00001065165,0.00003272936,0.0001751717,0.00001229844,0.000008658509,0.00005963305,0.00001048415,0.9766275,0.009427054,0.01111415,0.002511646,0.00001005714],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.009821696,0.0001426468,0.9865368,0.0001283853,0.00004796491,0.00004331215,0.0001053678,0.000943131,0.002230738],"genre_scores_gemma":[0.6363174,0.0003917529,0.3520419,0.0004218782,0.00007492738,0.000206015,0.0005411849,0.0008534544,0.009151542],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003506638,"threshold_uncertainty_score":0.01173085,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01629621085029089,"score_gpt":0.2260123027847563,"score_spread":0.2097160919344654,"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."}}