{"id":"W4409369895","doi":"10.1609/aaai.v39i3.32283","title":"Latent Diffusion-Enhanced Virtual Try-On via Optimized Pseudo-Label Generation","year":2025,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Handwritten Text Recognition Techniques","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"National Key Research and Development Program of China; Wuhan University of Technology; Wuhan University; National Natural Science Foundation of China","keywords":"Diffusion; Computer science; Physics; Thermodynamics","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":[0.0008636675,0.001070271,0.001031432,0.0006160742,0.0004281303,0.0008565061,0.001935711,0.001500563,0.003561297],"category_scores_gemma":[0.002846027,0.0005434189,0.0007913358,0.0004332656,0.001054013,0.001485343,0.001684337,0.001491215,0.0009006908],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006454834,"about_ca_system_score_gemma":0.0007480322,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001972094,"about_ca_topic_score_gemma":0.003734285,"domain_scores_codex":[0.9995379,0.0001073175,0.00001505137,0.0001390574,0.0001333915,0.00006734325],"domain_scores_gemma":[0.9985527,0.0006212957,0.0001449601,0.0003376573,0.000235892,0.0001073278],"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":[0.0003959411,0.0002071713,0.001309684,0.000199916,0.00006905892,0.0002184978,0.0002978267,0.587823,0.03922711,0.01275333,0.00621501,0.3512834],"study_design_scores_gemma":[0.00001268277,0.00002866741,0.00006474714,0.000004325741,0.000005287509,0.00003434617,0.000009075522,0.991734,0.005223684,0.002399719,0.0004777618,0.000005699365],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02920543,0.0001474234,0.9658017,0.0001785997,0.00005542509,0.00007550885,0.00006565361,0.002043152,0.002427088],"genre_scores_gemma":[0.5768017,0.0001417761,0.4132582,0.0003813651,0.00006363469,0.0002101723,0.000500752,0.0006127596,0.00802967],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003561297,"threshold_uncertainty_score":0.01191372,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05674266309541768,"score_gpt":0.2989897835099111,"score_spread":0.2422471204144934,"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."}}