{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004270342,0.0007873459,0.0004799574,0.0003883333,0.0001844032,0.0004461924,0.0008523888,0.0005967285,0.001673267],"category_scores_gemma":[0.00156117,0.0004185379,0.0005941234,0.0002841306,0.0006249514,0.0007522241,0.0008599205,0.001081534,0.0004212738],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005860173,"about_ca_system_score_gemma":0.0003349469,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002356596,"about_ca_topic_score_gemma":0.002834118,"domain_scores_codex":[0.9998105,0.00005221302,0.000006399429,0.00006132597,0.00004381799,0.00002561564],"domain_scores_gemma":[0.9996957,0.0001641994,0.0000355449,0.00005165876,0.00003021626,0.00002272423],"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":[0.00005679006,0.00002500788,0.0004785288,0.00004329718,0.00002931085,0.0001366258,0.00007269047,0.9237598,0.008628902,0.02489262,0.001799188,0.04007734],"study_design_scores_gemma":[0.00000295579,0.00001201709,0.00004630765,0.000003420879,0.000002409494,0.0000232178,0.000004299969,0.9923356,0.001036283,0.006060167,0.0004702534,0.000003018328],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01838941,0.0001833536,0.9784009,0.0001574748,0.00004091586,0.00004645642,0.0001101084,0.0007069669,0.001964384],"genre_scores_gemma":[0.7406781,0.0004348467,0.2520952,0.0002609146,0.00008676828,0.000175731,0.000715081,0.0003719388,0.005181388],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002356596,"threshold_uncertainty_score":0.005597711,"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."}}