{"id":"W2883638418","doi":"10.1109/tip.2019.2924554","title":"SiGAN: Siamese Generative Adversarial Network for Identity-Preserving Face Hallucination","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Image Processing","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":92,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"Ministry of Science and Technology, Taiwan","keywords":"Face (sociological concept); Adversarial system; Artificial intelligence; Computer science; Pattern recognition (psychology); Identity (music); Generative adversarial network; Facial recognition system; Face hallucination; Computer vision; Mathematics; Image (mathematics); Face detection; Linguistics","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004497223,0.0003040492,0.000288705,0.0002089054,0.0006848593,0.0008781521,0.001040259,0.0001260224,0.00002607561],"category_scores_gemma":[0.00004224405,0.0003221192,0.0001231378,0.0007897191,0.00007253085,0.007617868,0.00001827061,0.0003657021,0.00004109434],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001766153,"about_ca_system_score_gemma":0.0001928066,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009298512,"about_ca_topic_score_gemma":0.0000141969,"domain_scores_codex":[0.9978083,0.00007499041,0.0003821283,0.0007795573,0.0004338317,0.0005211481],"domain_scores_gemma":[0.9984642,0.0001616412,0.0002590644,0.000562957,0.0004602209,0.00009187265],"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":[0.0002546155,0.0005017637,0.00001837607,0.0008870531,0.000102639,0.00001623759,0.004382365,0.1249847,0.2797843,0.002036916,0.0009859245,0.5860452],"study_design_scores_gemma":[0.0007488371,0.0001511716,0.000007584525,0.0002383286,0.00002664175,0.00001101159,0.00007750202,0.8692436,0.111582,0.01706515,0.0004214736,0.0004267027],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0003484481,0.0002074083,0.9958479,0.0006643409,0.0007297994,0.0007682937,0.000008657826,0.0008833588,0.0005417877],"genre_scores_gemma":[0.2155433,0.00001366056,0.7832468,0.0003313433,0.0001367645,0.0001593503,0.000003151612,0.0000426718,0.0005229727],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.7442589,"threshold_uncertainty_score":0.9999231,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01596444325634168,"score_gpt":0.2962569778494424,"score_spread":0.2802925345931007,"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."}}