Deep Learning for Robot Perception and Cognition

Chapter 9: Progressive and compressive learning

Dat Thanh Tran^a; Moncef Gabbouj^a; Alexandros Iosifidis^b    aFaculty of Information Technology and Communication Sciences, Tampere University, Tampere, Finland^bDepartment of Electrical and Computer Engineering, Aarhus University, Aarhus, Denmark

Abstract

The expressive power of deep neural networks has enabled us to successfully tackle several modeling problems in computer vision, natural language processing, and financial forecasting in the last few years. Nowadays, neural networks achieving state-of-the-art (SoTA) performance in any field can be formed by hundreds of layers with millions of parameters. While achieving impressive performances, it is often required several days with high-end hardware ...