Date of Graduation
8-2024
Document Type
Thesis
Degree Name
Master of Science in Statistics and Analytics (MS)
Degree Level
Graduate
Department
Statistics and Analytics
Advisor/Mentor
Kaman, Tulin
Committee Member
Zhang, Qingyang
Second Committee Member
Plummer, Sean
Keywords
Deep neural networks; Parameters; Pruning; Sparsity
Abstract
Over the past decade, the widespread adoption of deep neural networks has been a breakthrough driven by significant computational advancements. Additionally, the number of parameters of those models is exponentially increasing for performing complex tasks and achieving better performance. However, in most practical cases, often there are constraints in the number of parameters due to limited resources in storage size and computational cost. Network pruning can lead to an optimal solution to this problem. In this thesis, I present supporting evidence to the hypothesis that higher sparsity leads to better performance for a convolution-based neural network. I perform performance studies to investigate the effect of the sparsity levels on the neural networks such as residual neural network (ResNet) and convolutional neural network (U-Net). I have used pre-iterative/ iterative pruning and trained encoder based architecture (ResNet) and encoder decoder based architecture (UNet) on the CIFAR10 and Electron Microscopy Dataset with equal parameter sizes on different sparsity levels. It concludes that with the constraint of fixed parameters, the sparse architecture performs better. A linear regression model has been used for statistical evidence.
Citation
Rawnaq, N. (2024). Sparse Neural Network to Enhance Performance Under Limited Parameter Constraints.. Graduate Theses and Dissertations Retrieved from https://scholarworks.uark.edu/etd/5412
