{"id":"W4408135470","doi":"10.1007/978-3-031-82153-0_26","title":"BA-GAN: A Boundary-Aware Generative Adversarial Network for Document Restoration","year":2025,"lang":"en","type":"book-chapter","venue":"Communications in computer and information science","topic":"Image Processing and 3D Reconstruction","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Generative grammar; Generative adversarial network; Boundary (topology); Adversarial system; Computer science; Information retrieval; Artificial intelligence; 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":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.001065689,0.0002136365,0.0002388965,0.0006358206,0.001239919,0.001393298,0.00185822,0.0001460864,0.000002962259],"category_scores_gemma":[0.00004562318,0.0002182567,0.0000555133,0.0004257279,0.0006411563,0.007301844,0.0009126771,0.0003167021,0.000009867043],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00024074,"about_ca_system_score_gemma":0.001216602,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008555681,"about_ca_topic_score_gemma":0.00002030191,"domain_scores_codex":[0.9984299,0.00003745373,0.0006504067,0.0003259941,0.0003162856,0.0002399581],"domain_scores_gemma":[0.9974073,0.0001909925,0.0003977037,0.001242237,0.0006952745,0.00006648658],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00000599811,0.000005670744,0.000005126908,0.00003773256,0.000006942901,8.906341e-8,0.0009203163,0.001011394,8.521621e-7,0.5150926,0.002921719,0.4799916],"study_design_scores_gemma":[0.0003929358,0.00005831717,0.00003398554,0.0002692693,0.000008941784,0.00001159377,0.00001471745,0.6162765,0.0000164473,0.0466112,0.3360523,0.0002538108],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.000002924111,0.0004043882,0.935117,0.00151827,0.001259946,0.0005170333,0.00001478688,0.00008486991,0.06108075],"genre_scores_gemma":[0.004275653,0.0013601,0.986863,0.001602712,0.0002998053,0.0001350449,0.0002272641,0.000009607959,0.005226798],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.6152651,"threshold_uncertainty_score":0.9996433,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02240356776309566,"score_gpt":0.2882971187412098,"score_spread":0.2658935509781142,"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."}}