Titlebook: Reproducible Research in Pattern Recognition; Third International Bertrand Kerautret,Miguel Colom,Hugues Talbot Conference proceedings 202

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Spatio-Temporal Convolutional Autoencoders for Perimeter Intrusion Detectionxisting camera based approaches relies on hand crafted rules, image based classification and supervised learning. In a real world intrusion detection system, we need to learn spatio-temporal features unsupervisely (as annotated data are very difficult to obtain) and use these features to detect intr

separate

Creating Emotion Recognition Algorithms Based on a Convolutional Neural Network for Sentiment Analys-dependent sentiment analysis in Slavic languages in specific cultural context, as well as the software implementation of the resulting network. Convolutional neural networks are easy to train and implement. To train them, a standard error back propagation algorithm is used, and because the filter w

transient-pain

Apoptosis

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Structure, Concept and Result Reproducibility of the Benchmark on Vesselness Filters filters are used to detect the presence of vessels in an image. There exists a wide variety of such filters and comparing their respective strengths and weaknesses is a non-trivial task, especially given the different contexts in which they are published. This benchmark was designed to ease such co

CANE

On the Implementation of Planar 3D Transfer Learning for End to End Unimodal MRI Unbalanced Data Segdata segmentation (ICPR 2020, Milan), which deals with a problem of multiple sclerosis lesion segmentation from a unimodal MRI flair brain scan by applying a planar 3D transfer learning backbone weights to an autoencoder segmentation neural network. Our source code is published online under an open-

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Reproducibility Aspects of Crack Detection as a Weakly-Supervised Problem: Towards Achieving Less Aned framework reproducible, the dataset reproducible, and the experiments reproducible. In addition, we argue that reproducibility is a step toward adoptable research, which is something all researchers should strive for. To promote future research, the implementation of the paper is publicly made av

companion

Reproducing the Sparse Huffman Address Map Compression for Deep Neural Networksllenge, a synergistic composition of network compression algorithms and compact storage of the compressed network has been recently presented, substantially preserving model accuracy. The proposed implementation, which we describe in this paper, offers different compression schemes (pruning, two typ

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Electrolysis

: Openly Teaching and Structuring Machine Learning Reproducibilityonses. Results suggest that students who do a reproduction project place more value on scientific reproductions and become more critical thinkers. Students and AI researchers agree that our online reproduction repository is valuable.