{"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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0003923874,0.0001620244,0.0002104461,0.0001267179,0.0001793553,0.0002228732,0.0005287121,0.00004825954,0.0001191445],"category_scores_gemma":[0.00004229811,0.0001284893,0.0001224757,0.0004721675,0.00003527523,0.0005052979,0.0002890951,0.00009427408,0.001831553],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002023073,"about_ca_system_score_gemma":0.00002116731,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000052981,"about_ca_topic_score_gemma":0.00000727469,"domain_scores_codex":[0.9985995,0.00008003523,0.0002387164,0.0004104558,0.0002395172,0.0004317777],"domain_scores_gemma":[0.9991705,0.0001324656,0.00005838823,0.0004269216,0.00009662077,0.0001150928],"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":[0.00005629805,0.0003175538,0.004844737,0.00005149812,0.0005560943,0.0006592519,0.001726267,0.1236229,0.2384234,0.03161386,0.2043188,0.3938094],"study_design_scores_gemma":[0.0005836135,0.00005438612,0.004041915,0.00001281698,0.000009777152,0.000009235256,0.00001715501,0.9452188,0.03991181,0.005284192,0.00455827,0.0002980088],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001735243,0.00006549213,0.9880064,0.004027138,0.0007340565,0.0001593715,0.000001590532,0.0006763283,0.004594415],"genre_scores_gemma":[0.9559366,0.0000673415,0.03720402,0.0006903763,0.0002100415,0.00001670671,0.000004149516,0.00001556848,0.005855217],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9542013,"threshold_uncertainty_score":0.9989457,"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."}}