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Reproducible Research in Pattern Recognition : Fourth International Workshop, RRPR 2022, Montreal, Canada, August 21, 2022, Revised Selected Papers / / edited by Bertrand Kerautret, Miguel Colom, Adrien Krähenbühl, Daniel Lopresti, Pascal Monasse, Benjamin Perret



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Autore: Kerautret Bertrand Visualizza persona
Titolo: Reproducible Research in Pattern Recognition : Fourth International Workshop, RRPR 2022, Montreal, Canada, August 21, 2022, Revised Selected Papers / / edited by Bertrand Kerautret, Miguel Colom, Adrien Krähenbühl, Daniel Lopresti, Pascal Monasse, Benjamin Perret Visualizza cluster
Pubblicazione: Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2023
Edizione: 1st ed. 2023.
Descrizione fisica: 1 online resource (127 pages)
Disciplina: 005.3
Soggetto topico: Application software
Computer engineering
Computer networks
Computers
Artificial intelligence
Computers, Special purpose
Computer and Information Systems Applications
Computer Engineering and Networks
Computing Milieux
Artificial Intelligence
Computer Communication Networks
Special Purpose and Application-Based Systems
Altri autori: ColomMiguel  
KrähenbühlAdrien  
LoprestiDaniel  
MonassePascal  
PerretBenjamin  
Nota di contenuto: Intro -- Preface -- Organization -- The Fuzzy Boundaries of Reproducibility (Lightweight Presentation Abstract) -- Contents -- Reproducible Research Framework -- Development Efforts for Reproducible Research: Platform, Library and Editorial Investment -- 1 Introduction -- 2 Reproducible Research Platform Updates -- 3 Reproducible Research Through Libraries -- 3.1 Library Experiences from Pattern Recognition, Image and Geometry Domains -- 3.2 Higra Library Development Feedback -- 4 Advanced Editorial Efforts -- 4.1 Improvements in the IPOL Journal -- 4.2 OVD-SaaS, a Spin-Off of IPOL for Industrial Applications -- 5 Conclusion -- References -- Reproducible Research Results -- Enhancing GNN Feature Modeling for Document Information Extraction Using Transformers -- 1 Introduction -- 2 Related Works -- 3 Proposed Model -- 3.1 Texts and Bounding Boxes -- 3.2 Features Assignment -- 3.3 Graph Construction -- 3.4 GNN Model -- 3.5 Model Prediction -- 4 Experiments -- 4.1 Dataset -- 4.2 Experimental Setup -- 4.3 Metrics -- 4.4 Results -- 4.5 Implementation Details -- 5 Conclusion -- References -- Short ICPR Companion Papers -- A Novel Pattern-Based Edit Distance for Automatic Log Parsing: Implementation and Reproducibility Notes -- 1 Introduction -- 2 Implementation Considerations -- 3 Installation Steps -- 4 Pattern Clustering Usage -- 4.1 Pattern Collection -- 4.2 Returned Value -- 4.3 Dropping Duplicated Pattern Automata -- 5 Experimental Setup -- 5.1 Drain and LogMine Integration -- 5.2 Loghub Dataset -- 5.3 Ground Truth -- 5.4 Experimental Parameters -- 5.5 Accuracy -- 6 Conclusion -- References -- Companion Paper: Deep Saliency Map Generators for Multispectral Video Classification -- 1 Introduction -- 2 Deep Saliency Map Generators -- 2.1 Grad-CAM -- 2.2 RISE -- 2.3 SIDU -- 3 Networks -- 3.1 3D-ResNet -- 3.2 Persistent Appearance Network.
4 Evaluation -- 4.1 Deletion Metric -- 4.2 Insertion Metric -- 5 Conclusion -- References -- On Challenging Aspects of Reproducibility in Deep Anomaly Detection -- 1 Introduction -- 2 Deep Anomaly Detection -- 3 Challenges for Reproducibility -- 3.1 Nondeterminism in Network Optimization -- 3.2 Sensitivity to Hyperparameters -- 3.3 Complexity -- 3.4 Dataset Selection -- 3.5 Resource Limitations -- 3.6 Dependencies -- 4 Complexity-Evidence Tradeoff -- 5 Conclusion -- References -- On the Implementation of Baselines and Lightweight Conditional Model Extrapolation (LIMES) Under Class-Prior Shift -- 1 Introduction -- 2 Dataset -- 3 Implementation -- 3.1 Preprocessing of Raw Json Files with Twitter Data -- 3.2 Embeddings -- 3.3 Machine Learning Models - Training and Evaluation -- 3.4 Running Experiments Efficiently -- 4 Reproducibility -- 5 Credibility of Results -- 6 Conclusions -- References -- Special Reproducibility Track from DGMM Event -- Combining Max-Tree and CNN for Segmentation of Cellular FIB-SEM Images -- 1 Introduction -- 2 State of the Art -- 3 Methods -- 3.1 Max-Tree -- 3.2 Segmentation -- 3.3 Evaluation Metrics -- 4 Experiments -- 4.1 Data -- 4.2 Results -- 4.3 Reproducibility -- 5 Conclusion -- A Appendix -- A.1 Results -- A.2 Example Preprocessing Visualization -- References -- Automatic Forest Road Extraction from LiDAR Data Using Convolutional Neural Networks*-12pt -- 1 Introduction -- 2 Method -- 2.1 Problem Statement -- 2.2 Previous Approach to Forest Road Extraction -- 2.3 Light DDCM-Net Architecture -- 3 Experimental Setup -- 3.1 Dataset -- 3.2 Network Training -- 4 Results and Discussions -- 5 Conclusion -- References -- Discussions Report Paper -- Promoting Reproducibility of Research Results in International Events (Report from the 4th RRPR)*-12pt -- 1 Introduction -- 2 Addressing RR at International Conferences.
2.1 Recent Proposals -- 2.2 New Ideas on Promoting RR at International Conferences -- 2.3 Impact of Efforts Encouraging RR in Conferences -- 3 Focus on Motivating RR -- 3.1 Recent Initiatives -- 3.2 Issues for Research Result Comparisons -- 3.3 Strengthening Reproducibility: From Publications to Teaching -- 4 Conclusion -- References -- Author Index.
Sommario/riassunto: This book constitutes the thoroughly refereed post-workshop proceedings of the 4th International Workshop on Reproducible Research in Pattern Recognition, RRPR 2022, held in Montreal, Canada, in August 2022. The 5 revised full papers presented together with 4 short papers, were carefully reviewed and selected from 9 submissions. The papers were organized into three main categories.
Titolo autorizzato: Reproducible Research in Pattern Recognition  Visualizza cluster
ISBN: 3-031-40773-3
Formato: Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione: Inglese
Record Nr.: 9910739460603321
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Serie: Lecture Notes in Computer Science, . 1611-3349 ; ; 14068