1. Comprehensive Survey of Model Compression and Speed up for Vision Transformers
- Author
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Chen, Feiyang, Luo, Ziqian, Zhou, Lisang, Pan, Xueting, and Jiang, Ying
- Subjects
Computer Science - Computer Vision and Pattern Recognition - Abstract
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.
- Published
- 2024