Reproducible Research in Pattern Recognition - Third International Workshop, RRPR 2021, Virtual Event, January 11, 2021, Revised Selected Papers

This book constitutes the thoroughly refereed post-workshop proceedings of the Third International Workshop on Reproducible Research in Pattern Recognition, RRPR 2021, held as a virtual event, in January 2021.
The 8 revised full papers, presented together with 6 short papers, were carefully reviewed and selected from 18 submissions.
The papers were organized into three main categories. The first contributions focused on reproducible research frameworks. The second category focused on reproducible research results and the last category included ICPR companion papers describing implementation and details that are an absolute requirement for reproducibility.

Sommario

Reproduced Papers.org: Openly Teaching and Structuring Machine Learning Reproducibility.- Reproducibility: Evaluating the Evaluations.- torchdistill: A Modular, Configuration-Driven Framework for Knowledge Distillation.- Spatio-Temporal Convolutional Autoencoders for Perimeter Intrusion Detection.- Creating Emotion Recognition Algorithms Based on a Convolutional Neural Network for Sentiment Analysis.- Tree Defect Segmentation Using Geometric Features and CNN.- Pith Estimation on Tree Log End Images.- Structure and Concept of the Benchmark for Vesselness Filters with Focus on Reproducibilty and Future Evaluations.- A Heuristic-Based Decision Tree for Connected Components Labeling of 3D Volumes: Implementation and Reproducibility Notes.- On the Implementation of Planar 3D Transfer Learning for End to End Unimodal MRI Unbalanced Data Segmentation.- Reproducibility Aspects of Crack Detection as a Weakly-Supervised Problem: Towards Achieving Less Annotation-Intensive Crack Detectors.- Reproducing the Sparse Huffman Address Map Compression for Deep Nural Networks.- Implementation of Genetic Pseudo Rehearsal.

Dettagli sul prodotto

• Con la collaborazione di: Migue Colom (Editore), Miguel Colom (Editore), Bertrand Kerautret (Editore), Adrien Krähenbühl (Editore), Adrien Krähenbühl et al (Editore), Daniel Lopresti (Editore), Pascal Monasse (Editore), Hugues Talbot (Editore)
• Editore: Springer, Berlin
• Lingue: Inglese
• Formato: Tascabile
• Pubblicazione: 01.07.2021
• EAN: 9783030764227
• ISBN: 978-3-0-3076422-7
• Pagine: 173
• Dimensioni: 155 mm x 10 mm x 235 mm
• Illustrazioni: X, 173 p. 72 illus., 54 illus. in color.
• Serie: Lecture Notes in Computer Science; Image Processing, Computer Vision, Pattern Recognition, and Graphics
• Categoria: Scienze naturali, medicina, informatica, tecnica > Informatica, EDP > Software applicativo