Multisource Labeled Data: an Opportunity for Training Deep Learning Networks
Lorenzo Bruzzone · 2019
This paper addresses the opportunities and the challenges offered by multisource labeled data in the framework of deep learning techniques. After a review of the types of multisource labeled data, the focus is devoted to their use for the training of deep learning classification architectures. The need to generate training sets containing a very large number of labeled samples pushes toward the exploitation of all the possible available sources of labeled data. This crucial topic is addressed by categorizing the approaches to the collection of labeled data and presenting a framework for characterizing and modeling their information content and uncertainties to be used in the training of processing algorithms. The framework defines the main expected properties of large multisource training sets and relates them to both the characteristics of different data sources and the possible learning paradigms for the training of a deep architecture.