RED : Looking for Redundancies for Data-Free Structured Compression of Deep Neural Networks

31 May 2021

Papers citing "RED : Looking for Redundancies for Data-Free Structured Compression of Deep Neural Networks"

8 / 8 papers shown

Title
Network Memory Footprint Compression Through Jointly Learnable Codebooks and Mappings Vittorio Giammarino Arnaud Dapogny Kévin Bailly MQ 24 1 0 29 Sep 2023
SAfER: Layer-Level Sensitivity Assessment for Efficient and Robust Neural Network Inference Edouard Yvinec Arnaud Dapogny Kévin Bailly Xavier Fischer AAML 16 2 0 09 Aug 2023
Accurate Retraining-free Pruning for Pretrained Encoder-based Language Models Seungcheol Park Ho-Jin Choi U. Kang VLM 40 5 0 07 Aug 2023
Approximate Computing Survey, Part II: Application-Specific & Architectural Approximation Techniques and Applications Vasileios Leon Muhammad Abdullah Hanif Giorgos Armeniakos Xun Jiao Muhammad Shafique K. Pekmestzi Dimitrios Soudris 39 3 0 20 Jul 2023
SInGE: Sparsity via Integrated Gradients Estimation of Neuron Relevance Edouard Yvinec Arnaud Dapogny Matthieu Cord Kévin Bailly 42 9 0 08 Jul 2022
RED++ : Data-Free Pruning of Deep Neural Networks via Input Splitting and Output Merging Edouard Yvinec Arnaud Dapogny Matthieu Cord Kévin Bailly 28 15 0 30 Sep 2021
SCOP: Scientific Control for Reliable Neural Network Pruning Yehui Tang Yunhe Wang Yixing Xu Dacheng Tao Chunjing Xu Chao Xu Chang Xu AAML 50 167 0 21 Oct 2020
Comparing Rewinding and Fine-tuning in Neural Network Pruning Alex Renda Jonathan Frankle Michael Carbin 229 383 0 05 Mar 2020