Automatic human sleep stage scoring using Deep Neural Networks

2 code implementations • 6 Nov 2018 • Alexander Malafeev, Dmitry Laptev, Stefan Bauer, Ximena Omlin, Aleksandra Wierzbicka, Adam Wichniak, Wojciech Jernajczyk, Robert Riener, Joachim M. Buhmann and Peter Achermann

The classification of sleep stages is the first and an important step in the quantitative analysis of polysomnographic recordings.

Classification General Classification

TI-POOLING: transformation-invariant pooling for feature learning in Convolutional Neural Networks

1 code implementation • CVPR 2016 • Dmitry Laptev, Nikolay Savinov, Joachim M. Buhmann, Marc Pollefeys

This more efficient use of training data results in better performance on popular benchmark datasets with smaller number of parameters when comparing to standard convolutional neural networks with dataset augmentation and to other baselines.

Transformation-Invariant Convolutional Jungles

no code implementations • CVPR 2015 • Dmitry Laptev, Joachim M. Buhmann

Many Computer Vision problems arise from information processing of data sources with nuisance variances like scale, orientation, contrast, perspective foreshortening or - in medical imaging - staining and local warping.

Face Recognition Image Classification