Semi-supervised learning through adversary networks for baseline detection
| Autor: | Belaid Abdel, Karpinski Romain |
|---|---|
| Přispěvatelé: | Recognition of writing and analysis of documents (READ), Department of Natural Language Processing & Knowledge Discovery (LORIA - NLPKD), Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS), Belaid, Abdel |
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI]
business.industry Computer science 020207 software engineering 02 engineering and technology Semi-supervised learning [INFO] Computer Science [cs] Adversary Machine learning computer.software_genre Residual Semantic segmentation [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI] 0202 electrical engineering electronic engineering information engineering Feature (machine learning) 020201 artificial intelligence & image processing [INFO]Computer Science [cs] Artificial intelligence Adversary networks business ARU-Net computer |
| Zdroj: | ICDAR-WML ICDAR-WML, Sep 2019, Sydney, Australia WML@ICDAR |
| Popis: | International audience; The aim of this paper is to propose a new strategy adapted to the semantic segmentation of document images in order to extract baselines. Inspired by the work of Grüning [7], we used a convolutional model with residual layers enriched by an attention mechanism, called ARU-Net, a post-processing for the agglomeration of predictions and a data augmentation to enrich the database. Then, to consolidate the ARU-Net and help explicitly model dependencies between feature maps, we added a module of "Squeeze and Excitation" as proposed by Hu et al. [9]. Finally, to exploit the amount of unrated data available, we used a semi-supervised learning, based on ARU-Net, through the use of adversary networks. This approach has shown some interesting predictive qualities, compared to Grüning's work, with easier processing and less task-specific error correction. The resulting performance improvement is a success. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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