{"id":"W4312371981","doi":"10.1109/icpr56361.2022.9956534","title":"Frank-Wolfe-based Multi-task Learning for Historical Document Restoration","year":2022,"lang":"en","type":"article","venue":"2022 26th International Conference on Pattern Recognition (ICPR)","topic":"Handwritten Text Recognition Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Computer science; Artificial intelligence; Task (project management); Machine learning; Inference; Supervised learning; Deep learning; Artificial neural network","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","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0008635087,0.0003138578,0.0002826735,0.0006329161,0.0006496361,0.0003494689,0.001117321,0.00008967135,0.002999246],"category_scores_gemma":[0.000192219,0.0003634007,0.0002251461,0.0003241057,0.00003704699,0.0005603575,0.0002915288,0.0007029879,0.000223588],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001415624,"about_ca_system_score_gemma":0.0002300823,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001179553,"about_ca_topic_score_gemma":0.00003171793,"domain_scores_codex":[0.9965303,0.0004151093,0.0006652395,0.0008879235,0.001125051,0.0003763813],"domain_scores_gemma":[0.9980624,0.0002374224,0.0004806044,0.0003740785,0.000698504,0.0001469717],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004215524,0.001396344,0.0009617109,0.0000748317,0.000151442,0.00008478131,0.0008602837,0.0006975876,0.006156467,0.007830201,0.0240465,0.9573183],"study_design_scores_gemma":[0.008613109,0.004738861,0.001996408,0.0003400949,0.00008435871,0.0001076665,0.0005662705,0.6661914,0.02655856,0.03847602,0.2496821,0.002645123],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005017817,0.00003157546,0.9812007,0.00662704,0.002636551,0.0008942529,0.0001989914,0.0006511167,0.002741968],"genre_scores_gemma":[0.9749277,0.00003413402,0.01499042,0.003459914,0.0002903189,0.002832504,0.001511539,0.00004618863,0.001907285],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9699099,"threshold_uncertainty_score":0.9998818,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07621091900896396,"score_gpt":0.3065656147011682,"score_spread":0.2303546956922043,"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."}}