Contributions to deep learning methodologies
View/ Open
Date
2018-10-26Author
Bazrafkan, Shabab
Metadata
Show full item recordUsage
This item's downloads: 1035 (view details)
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.