EPSD: Early Pruning with Self-Distillation for Efficient Model Compression

31 January 2024

Papers citing "EPSD: Early Pruning with Self-Distillation for Efficient Model Compression"

7 / 7 papers shown

Title
Optimising TinyML with Quantization and Distillation of Transformer and Mamba Models for Indoor Localisation on Edge Devices Thanaphon Suwannaphong Ferdian Jovan I. Craddock Ryan McConville Mamba 66 1 0 12 Dec 2024
LLMCBench: Benchmarking Large Language Model Compression for Efficient Deployment Ge Yang Changyi He J. Guo Jianyu Wu Yifu Ding Aishan Liu Haotong Qin Pengliang Ji Xianglong Liu MQ 31 4 0 28 Oct 2024
CP $^3$ : Channel Pruning Plug-in for Point-based Networks Yaomin Huang Ning Liu Zhengping Che Zhiyuan Xu Chaomin Shen Yaxin Peng Guixu Zhang Xinmei Liu Feifei Feng Jian Tang 3DPC 31 14 0 23 Mar 2023
MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer Sachin Mehta Mohammad Rastegari ViT 189 1,148 0 05 Oct 2021
Prune Your Model Before Distill It Jinhyuk Park Albert No VLM 38 27 0 30 Sep 2021
Distilling Knowledge via Knowledge Review Pengguang Chen Shu-Lin Liu Hengshuang Zhao Jiaya Jia 147 416 0 19 Apr 2021
Comparing Rewinding and Fine-tuning in Neural Network Pruning Alex Renda Jonathan Frankle Michael Carbin 222 382 0 05 Mar 2020