Network Pruning That Matters: A Case Study on Retraining Variants

7 May 2021

Papers citing "Network Pruning That Matters: A Case Study on Retraining Variants"

10 / 10 papers shown

Title
Straightforward Layer-wise Pruning for More Efficient Visual Adaptation Ruizi Han Jinglei Tang 45 1 0 19 Jul 2024
PERP: Rethinking the Prune-Retrain Paradigm in the Era of LLMs Max Zimmer Megi Andoni Christoph Spiegel S. Pokutta VLM 50 10 0 23 Dec 2023
Unlearning with Fisher Masking Yufang Liu Changzhi Sun Yuanbin Wu Aimin Zhou MU 21 5 0 09 Oct 2023
Magnitude Attention-based Dynamic Pruning Jihye Back Namhyuk Ahn Jang-Hyun Kim 25 2 0 08 Jun 2023
Network Pruning Spaces Xuanyu He Yu-I Yang Ran Song Jiachen Pu Conggang Hu Feijun Jiang Wei Zhang Huanghao Ding 3DPC 27 0 0 19 Apr 2023
Why is the State of Neural Network Pruning so Confusing? On the Fairness, Comparison Setup, and Trainability in Network Pruning Huan Wang Can Qin Yue Bai Yun Fu 32 20 0 12 Jan 2023
Sparse Double Descent: Where Network Pruning Aggravates Overfitting Zhengqi He Zeke Xie Quanzhi Zhu Zengchang Qin 69 27 0 17 Jun 2022
Compression-aware Training of Neural Networks using Frank-Wolfe Max Zimmer Christoph Spiegel S. Pokutta 16 9 0 24 May 2022
1xN Pattern for Pruning Convolutional Neural Networks Mingbao Lin Yu-xin Zhang Yuchao Li Bohong Chen Fei Chao Mengdi Wang Shen Li Yonghong Tian Rongrong Ji 3DPC 31 40 0 31 May 2021
Comparing Rewinding and Fine-tuning in Neural Network Pruning Alex Renda Jonathan Frankle Michael Carbin 224 382 0 05 Mar 2020