{"id":"W3020030698","doi":"10.1109/cvpr42600.2020.00518","title":"Disentangled Image Generation Through Structured Noise Injection","year":2020,"lang":"en","type":"preprint","venue":"","topic":"Generative Adversarial Networks and Image Synthesis","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Kootenay Association for Science & Technology","funders":"","keywords":"Computer science; Noise (video); Generator (circuit theory); Image (mathematics); Code (set theory); Face (sociological concept); Object (grammar); Grid; Artificial intelligence; Generative model; Space (punctuation); Layer (electronics); Computer vision; Pattern recognition (psychology); Generative grammar; Power (physics); 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":[0.001353007,0.00159526,0.0007086604,0.0004712528,0.0002599925,0.001138018,0.001506521,0.001246815,0.00311239],"category_scores_gemma":[0.00516839,0.0005075437,0.0008036413,0.0003760083,0.001243477,0.001568247,0.002291145,0.002291008,0.001054068],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006802389,"about_ca_system_score_gemma":0.0004914133,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001078197,"about_ca_topic_score_gemma":0.001724316,"domain_scores_codex":[0.9992396,0.000219833,0.00002453596,0.0002599013,0.0001741658,0.00008205719],"domain_scores_gemma":[0.9983687,0.0008546941,0.0001236867,0.0004541636,0.0001173606,0.00008148757],"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":[0.0004308708,0.0001432447,0.001288604,0.0001419218,0.00009382075,0.0002754859,0.0001528727,0.8273955,0.02612374,0.02852654,0.003328956,0.1120984],"study_design_scores_gemma":[0.00002038819,0.0000444876,0.0001161089,0.00001186924,0.00000869353,0.00005742356,0.000008640152,0.9778135,0.008529176,0.01226812,0.001112432,0.000009098967],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03283537,0.0003116732,0.9608167,0.000325182,0.00007339655,0.00008149104,0.000182824,0.001561381,0.003811958],"genre_scores_gemma":[0.7380619,0.0002659857,0.2515426,0.0005998981,0.00006830461,0.00022782,0.000830321,0.0005962446,0.007806984],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00311239,"threshold_uncertainty_score":0.01041192,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03524480576759212,"score_gpt":0.2634259380580776,"score_spread":0.2281811322904855,"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."}}