Digya Acharya

Date of Award


Document Type


Degree Name

Master of Science (MS)


Computer Science

Committee Chair

Chaity Banerjee

Committee Member

Letha Etzkorn

Committee Member

Vineetha Menon


Deep learning (Machine learning), Pattern recognition systems, Image processing


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.



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