An Overview of Model Compression Techniques for Deep Neural Networks

Author(s)	Vishakha Agrawal
Country	United States
Abstract	As deep learning models continue to grow in size and complexity, the need for efficient deployment on resource- constrained devices becomes increasingly important. We examine various compression methods including pruning, quantization, knowledge distillation, and low-rank factorization. Our analysis covers the theoretical foundations, implementation challenges, and practical trade-offs of each approach. This paper presents a comprehensive overview of model compression techniques for deep neural networks (DNNs). We also discuss emerging trends and future research directions in model compression.
Keywords	Pruning, Quantization, Knowledge Distillation, and Low-rank factorization
Field	Engineering
Published In	Volume 1, Issue 3, November 2020
Published On	2020-11-05
Cite This	An Overview of Model Compression Techniques for Deep Neural Networks - Vishakha Agrawal - IJLRP Volume 1, Issue 3, November 2020. DOI 10.5281/zenodo.14673062
DOI	https://doi.org/10.5281/zenodo.14673062
Short DOI	https://doi.org/g8z64d

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