{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008131875,0.0009410375,0.0005665095,0.0003009186,0.0002030375,0.0004074348,0.001082375,0.0006418243,0.002205335],"category_scores_gemma":[0.0016222,0.0002825716,0.0006075751,0.0002187439,0.0007037005,0.0006078103,0.001390481,0.001401277,0.0006742631],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004215769,"about_ca_system_score_gemma":0.0004517527,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001668786,"about_ca_topic_score_gemma":0.002293722,"domain_scores_codex":[0.9997074,0.00009354927,0.000009251067,0.00006626511,0.00009034393,0.00003318136],"domain_scores_gemma":[0.9995905,0.0001993196,0.00003553132,0.00008273075,0.00006296526,0.00002913652],"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":[0.0001820949,0.00007872518,0.0007788103,0.00008359507,0.00008979328,0.000196216,0.00006244017,0.8291765,0.01539967,0.01162066,0.005462925,0.1368686],"study_design_scores_gemma":[0.000004112727,0.00001850439,0.00005056606,0.000002980541,0.000003549269,0.00004160554,0.000003299077,0.9951445,0.001787432,0.002484835,0.0004541306,0.000004335438],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.009493438,0.0002491589,0.9872039,0.0001381325,0.00005020369,0.00003850734,0.00008062856,0.0009448291,0.001801218],"genre_scores_gemma":[0.7215984,0.000490416,0.2660457,0.0006099517,0.00008068549,0.0002012799,0.0007608841,0.0002861048,0.009926512],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002205335,"threshold_uncertainty_score":0.007377625,"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."}}