Guest Editorial: Deep Learning

Abstract

Deep Learning methods aim at learning feature hierarchies. Applications of deep learning to vision tasks date back to convolutional networks in the early 1990s. These methods have been the subject of a recent surge of interest for two main reasons: when labeled data is scarce, unsupervised learning algorithms can learn useful feature hierarchies. When labeled data is abundant, supervised methods can be used to train very large networks on very large datasets through the use of high-performance computers. Such large networks have been shown to outperform previous state-of-theart methods on several perceptual tasks, including categorylevel object recognition, object detection and semantic segmentation. In “Stacked Predictive Sparse Decomposition for Classification of Histology Sections” (doi:10.1007/s11263-0140790-9) the authors propose the use of an unsupervised feature learning algorithm for the analysis of biological tissue imagery. Biomedical applications is one domainwhere unsupervised learning can be very useful because of the paucity of labeled images available at training time.