Date of Award
2022
Document Type
Thesis
Degree Name
Master of Science (MS)
Department
Computer Science
Committee Chair
Chaity Banerjee
Committee Member
Letha Etzkorn
Committee Member
Vineetha Menon
Subject(s)
Deep learning (Machine learning), Pattern recognition systems, Image processing
Abstract
In this thesis we address the problem of consensus based multi-label classification. In the general multi-label classification problem, an input is assigned to multiple classes without any constraints. However, there are applications where the multi-label classification needs to be solved under the constraint of a consensus among the assigned labels. We address this problem here using a deep learning approach. We conduct experiments with the MNIST dataset and establish the possibility of using such approaches for traditional multi-class problems. Furthermore, we demonstrate the use of our consensus based classifier in a generative adversarial framework for training a generator for handwritten digits. Our approach results in a lower loss and better quality generated images.
Recommended Citation
Acharya, Digya, "A deep learning framework for consensus based multilabel classification and image generation" (2022). Theses. 394.
https://louis.uah.edu/uah-theses/394