Reproducible Research in Pattern Recognition

Third International Workshop, RRPR 2021, Virtual Event, January 11, 2021, Revised Selected Papers

AvBertrand Kerautret,Miguel Colom

Häftad, Engelska, 2021

Del 12636 i serien Lecture Notes in Computer Science

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Beskrivning

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.

Produktinformation

Utgivningsdatum:2021-05-14
Mått:155 x 235 x 11 mm
Vikt:289 g
Format:Häftad
Språk:Engelska
Serie:Lecture Notes in Computer Science
Antal sidor:173
Förlag:Springer Nature Switzerland AG
ISBN:9783030764227

Utforska kategorier

Systemvetenskap och AI inom Data och IT
Nätverk och kommunikation inom Data och IT
Hårdvara inom Data och IT

Innehållsförteckning

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.