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"

11 / 11 papers shown

Title
Straightforward Layer-wise Pruning for More Efficient Visual Adaptation Ruizi Han Jinglei Tang 58 1 0 19 Jul 2024
Iterative Filter Pruning for Concatenation-based CNN Architectures Svetlana Pavlitska Oliver Bagge Federico Nicolás Peccia Toghrul Mammadov J. Marius Zöllner VLM 3DPC 40 2 0 04 May 2024
PERP: Rethinking the Prune-Retrain Paradigm in the Era of LLMs Max Zimmer Megi Andoni Christoph Spiegel Sebastian Pokutta VLM 52 10 0 23 Dec 2023
Unlearning with Fisher Masking Yufang Liu Changzhi Sun Yuanbin Wu Aimin Zhou MU 23 5 0 09 Oct 2023
Magnitude Attention-based Dynamic Pruning Jihye Back Namhyuk Ahn Jang-Hyun Kim 32 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 35 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 37 20 0 12 Jan 2023
Sparse Double Descent: Where Network Pruning Aggravates Overfitting Zhengqi He Zeke Xie Quanzhi Zhu Zengchang Qin 79 27 0 17 Jun 2022
Compression-aware Training of Neural Networks using Frank-Wolfe Max Zimmer Christoph Spiegel Sebastian Pokutta 29 9 0 24 May 2022
1xN Pattern for Pruning Convolutional Neural Networks Mingbao Lin Yu-xin Zhang Yuchao Li Bohong Chen Rongrong Ji Mengdi Wang Shen Li Yonghong Tian Rongrong Ji 3DPC 33 40 0 31 May 2021
Comparing Rewinding and Fine-tuning in Neural Network Pruning Alex Renda Jonathan Frankle Michael Carbin 229 383 0 05 Mar 2020