{"id":"W4402716110","doi":"10.1109/cvpr52733.2024.00752","title":"StyleCineGAN: Landscape Cinemagraph Generation Using a Pre-trained StyleGAN","year":2024,"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":"Kootenay Association for Science & Technology","funders":"","keywords":"Computer science; Artificial intelligence","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":[0.0002590132,0.0006820354,0.0002845481,0.0003039501,0.0001370703,0.0003978004,0.0005866781,0.0004950355,0.00499177],"category_scores_gemma":[0.001005366,0.0002462185,0.0004462996,0.000145329,0.0003094855,0.0005311951,0.0005678494,0.0007909691,0.001101738],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000281295,"about_ca_system_score_gemma":0.000232599,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008409779,"about_ca_topic_score_gemma":0.001731212,"domain_scores_codex":[0.9998924,0.00001561461,0.000003467622,0.00003941553,0.00003473681,0.00001447097],"domain_scores_gemma":[0.9998111,0.00007290426,0.00001293172,0.00005297927,0.00003554724,0.00001463214],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002581886,0.0001783925,0.00164802,0.0002852749,0.0001061596,0.0004484963,0.0001998359,0.3097661,0.142433,0.01932428,0.01764913,0.5077031],"study_design_scores_gemma":[0.00002636745,0.00007292419,0.0003883995,0.00001886029,0.0000159544,0.0002240105,0.00001789922,0.9542564,0.03282045,0.004382202,0.007761141,0.0000154723],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02555735,0.0002310847,0.9604983,0.0002014102,0.0001813571,0.000163321,0.0002724095,0.004408651,0.008486181],"genre_scores_gemma":[0.5059069,0.000300792,0.4744706,0.0004866773,0.00008331046,0.0003004804,0.00111379,0.001194531,0.01614301],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00499177,"threshold_uncertainty_score":0.01669914,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02235905150588658,"score_gpt":0.261806481187728,"score_spread":0.2394474296818415,"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."}}