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.
@article{arxiv.2404.10407,
title = {Comprehensive Survey of Model Compression and Speed up for Vision Transformers},
author = {Feiyang Chen and Ziqian Luo and Lisang Zhou and Xueting Pan and Ying Jiang},
journal= {arXiv preprint arXiv:2404.10407},
year = {2024}
}