{"id":"W2805274379","doi":"10.1007/978-3-030-17798-0_41","title":"CapsGAN: Using Dynamic Routing for Generative Adversarial Networks","year":2019,"lang":"en","type":"book-chapter","venue":"Advances in intelligent systems and computing","topic":"Generative Adversarial Networks and Image Synthesis","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Normalization (sociology); Artificial intelligence; MNIST database; Clipping (morphology); Knot (papermaking); Image translation; Computer vision; Algorithm; Pattern recognition (psychology); Image (mathematics); Deep learning","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006860704,0.0006248759,0.0009861251,0.0002219829,0.0003335153,0.0003601068,0.0006282842,0.0003427237,0.000004502916],"category_scores_gemma":[0.00004146611,0.0006079146,0.0002145828,0.000103077,0.00008926613,0.0005519115,0.0004637857,0.0004451621,0.000005598243],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002829016,"about_ca_system_score_gemma":0.0001218985,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006553769,"about_ca_topic_score_gemma":0.00005919001,"domain_scores_codex":[0.9968357,0.00009700286,0.001008462,0.001149998,0.0002833889,0.0006254287],"domain_scores_gemma":[0.9977705,0.0006182508,0.000793692,0.0005101812,0.0002037115,0.0001036719],"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":[0.00001451147,0.000008937983,0.00002011728,0.0001112466,0.00007277245,0.000009892246,0.000286245,0.8074314,0.00002017505,0.1302268,0.00002705904,0.06177091],"study_design_scores_gemma":[0.0002965158,0.00009990644,0.000001622574,0.001218752,0.00003467527,0.00002373249,0.0001058126,0.9741911,0.00003218622,0.002129184,0.02121955,0.0006470186],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.000039134,0.02453434,0.9594656,0.00002635995,0.005966638,0.001178165,0.00001042381,0.00006412995,0.008715243],"genre_scores_gemma":[0.7904942,0.008313793,0.158073,0.0003494842,0.005694909,0.00005009857,0.00009800823,0.0003295136,0.03659697],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8013926,"threshold_uncertainty_score":0.9996372,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01952462030379244,"score_gpt":0.2670360464849232,"score_spread":0.2475114261811308,"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."}}