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2006.12279
Cited By

Revisiting Loss Modelling for Unstructured Pruning

Revisiting Loss Modelling for Unstructured Pruning

22 June 2020

Pascal Vincent

ArXiv (abs)PDF HTML

Papers citing "Revisiting Loss Modelling for Unstructured Pruning"

6 / 6 papers shown

On Efficient Variants of Segment Anything Model: A Survey

On Efficient Variants of Segment Anything Model: A SurveyInternational Journal of Computer Vision (IJCV), 2024

515

19

0

07 Oct 2024

Unified Data-Free Compression: Pruning and Quantization without
Fine-Tuning

Unified Data-Free Compression: Pruning and Quantization without Fine-TuningIEEE International Conference on Computer Vision (ICCV), 2023

Jun Chen

Xintian Shen

234

21

0

14 Aug 2023

Sparse then Prune: Toward Efficient Vision Transformers

Sparse then Prune: Toward Efficient Vision Transformers

127

4

0

22 Jul 2023

Person Detection Using an Ultra Low-resolution Thermal Imager on a
Low-cost MCU

Person Detection Using an Ultra Low-resolution Thermal Imager on a Low-cost MCUImage and Vision Computing New Zealand (IVCNZ), 2022

Maarten Vandersteegen

Kristof Van Beeck

137

2

0

16 Dec 2022

Cyclical Pruning for Sparse Neural Networks

Cyclical Pruning for Sparse Neural Networks

Tijmen Blankevoort

188

15

0

02 Feb 2022

Data-Driven Low-Rank Neural Network Compression

Data-Driven Low-Rank Neural Network Compression

D. Papadimitriou

Swayambhoo Jain

235

8

0

13 Jul 2021