{"id":"W4289146962","doi":"10.52591/lxai201812036","title":"Generating Videos by Traversing Image Manifolds Learned by GANs","year":2018,"lang":"en","type":"article","venue":"","topic":"Generative Adversarial Networks and Image Synthesis","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"","keywords":"Generator (circuit theory); Discriminator; Computer science; Generative grammar; Artificial intelligence; Traverse; Image (mathematics); Adversarial system; Computer vision; Manifold (fluid mechanics); Sequence (biology); Scheme (mathematics); Generative model; 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":[],"consensus_categories":[],"category_scores_codex":[0.0002682709,0.0001826568,0.0001652248,0.00003995882,0.0004251186,0.000461562,0.0005640256,0.00006573153,0.0003684082],"category_scores_gemma":[0.00003575901,0.0001626633,0.00007027281,0.0002495322,0.00009814859,0.0008040495,0.0001581275,0.00009460572,0.0002157781],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003682537,"about_ca_system_score_gemma":0.00002969477,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001606381,"about_ca_topic_score_gemma":0.00002351764,"domain_scores_codex":[0.9985295,0.0001144641,0.0002160242,0.0005036739,0.0002236522,0.0004127159],"domain_scores_gemma":[0.9992715,0.00005979982,0.0000707133,0.0003668449,0.0001076498,0.0001235113],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000002911455,0.00003124438,0.00001493528,0.000002119988,0.00001899406,0.000003522893,0.0003279474,0.00008940841,0.6948884,0.0005728208,0.2806441,0.02340362],"study_design_scores_gemma":[0.0002641198,0.000124887,0.00001278815,0.000008317587,0.000007805284,0.000005580068,0.00009097002,0.5254543,0.4374813,0.0001773144,0.03608955,0.0002830309],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003484474,0.00008960235,0.9792085,0.001633404,0.0003268437,0.00009774085,0.000004358807,0.0001543606,0.01500066],"genre_scores_gemma":[0.6013423,0.00001872163,0.390299,0.002125376,0.0005183743,0.000005148707,0.000007870325,0.0000208436,0.00566232],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5978578,"threshold_uncertainty_score":0.6633219,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0118816471500196,"score_gpt":0.2356378222970483,"score_spread":0.2237561751470287,"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."}}