Open Access. Powered by Scholars. Published by Universities.®
Articles 1 - 2 of 2
Full-Text Articles in Engineering
Learning Models For Corrupted Multi-Dimensional Data: Fundamental Limits And Algorithms, Ishan Jindal
Learning Models For Corrupted Multi-Dimensional Data: Fundamental Limits And Algorithms, Ishan Jindal
Wayne State University Dissertations
Developing machine learning models for unstructured multi-dimensional datasets such as datasets with unreliable labels and noisy multi-dimensional signals with or without missing information have becoming a central necessity. We are not always fortunate enough to get noise-free datasets for developing classification and representation models. Though there is a number of techniques available to deal with noisy datasets, these methods do not exploit the multi-dimensional structures of the signals, which could be used to improve the overall classification and representation performance of the model.
In this thesis, we develop a Kronecker-structure (K-S) subspace model that exploits the multi-dimensional structure of the …
Learning Convolutional Neural Network For Face Verification, Elaheh Rashedi
Learning Convolutional Neural Network For Face Verification, Elaheh Rashedi
Wayne State University Dissertations
Convolutional neural networks (ConvNet) have improved the state of the art in many applications. Face recognition tasks, for example, have seen a significantly improved performance due to ConvNets. However, less attention has been given to video-based face recognition. Here, we make three contributions along these lines.
First, we proposed a ConvNet-based system for long-term face tracking from videos. Through taking advantage of pre-trained deep learning models on big data, we developed a novel system for accurate video face tracking in the unconstrained environments depicting various people and objects moving in and out of the frame. In the proposed system, we …