Layer-wise Learning of Feature Hierarchies
Hannes Schulz, Sven Behnke · 2012
Hierarchical neural networks for object recog- nition have a long history. In recent years, novel methods for incrementally learning a hierarchy of features from unlabeled inputs were proposed as good starting point for supervised training. These deep learning methods| together with the advances of parallel computers|made it possible to successfully attack problems that were not practical before, in terms of depth and input size. In this article, we introduce the reader to the basic concepts of deep learning, discuss selected methods in detail, and present application examples from computer vision and speech recognition.