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Regular version of the site

Book chapter

Compressing deep convolutional neural networks in visual emotion recognition

P. 207-213.
A. G. Rassadin, A. V. Savchenko.
In this paper, we consider the problem of insufficient runtime and memory space complexities of deep convolutional neural networks for visual emotion recognition. A survey of recent compression methods and efficient neural networks architectures is provided. We experimentally compare the computational speed and memory consumption during the training and the inference stages of such methods as the weights matrix decomposition, binarization and hashing. It is shown that the most efficient optimization can be achieved with the matrices decomposition and hashing. Finally, we explore the possibility to distill the knowledge from the large neural network, if only large unlabeled sample of facial images is available.

In book

Edited by: V. Myasnikov, V. Sergeyev, V. Fedoseev et al. Vol. 1901: Proceedings of the International conference Information Technology and Nanotechnology. Session Image Processing, Geoinformation Technology and Information Security. 2017.