Contributions to deep learning methodologies

Abstract

In recent years the Deep Neural Networks (DNN) has been using widely in a big range of machine learning and data-mining purposes. This pattern recognition approach can handle highly nonlinear problems. In this work, three main contributions to DNN are presented. 1- A method called Semi Parallel Deep Neural Networks (SPDNN) is introduced wherein several deep architectures are mixed and merged using graph contraction technique to take advantage of all the parent networks. 2- The importance of data is investigated in several attempts and an augmentation technique know as Smart Augmentation is presented. 3- To extract more information from a database, multiple works on Generative Adversarial Networks (GAN) are given wherein the joint distribution of data and its ground truth is approximated and in other projects conditional generators for classification and regression problems are trained and tested.