Comprehensive Survey of Model Compression and Speed up for Vision Transformers

作者：Feiyang Chen, Ziqian Luo, Lisang Zhou, Xueting Pan, Ying Jiang · 发表于：Journal of Information Technology and Policy · 年份：2024 · DOI：10.62836/jitp.v1i1.156 · 被引用次数：28 · 研究领域：CCD and CMOS Imaging Sensors、Neural Networks and Applications、Infrared Target Detection Methodologies

Vision Transformers (ViT) have marked a paradigm shift in computer vision, outperforming state-of-the-art models across diverse tasks. However, their practical deployment is hampered by high computational and memory demands. This study addresses the challenge by evaluating four primary model compression techniques: quantization, low-rank approximation, knowledge distillation, and pruning. We methodically analyze and compare the efficacy of these techniques and their combinations in optimizing ViTs for resource-constrained environments. Our comprehensive experimental evaluation demonstrates that these methods facilitate a balanced compromise between model accuracy and computational efficiency, paving the way for wider application in edge computing devices.