Interspace Pruning: Using Adaptive Filter Representations to Improve Training of Sparse CNNs

15 March 2022

Papers citing "Interspace Pruning: Using Adaptive Filter Representations to Improve Training of Sparse CNNs"

7 / 7 papers shown

Title
PROM: Prioritize Reduction of Multiplications Over Lower Bit-Widths for Efficient CNNs Lukas Meiner Jens Mehnert A. P. Condurache MQ 42 0 0 06 May 2025
Advancing Weight and Channel Sparsification with Enhanced Saliency Xinglong Sun Maying Shen Hongxu Yin Lei Mao Pavlo Molchanov Jose M. Alvarez 46 1 0 05 Feb 2025
TULIP: Transformer for Upsampling of LiDAR Point Clouds Bin Yang Patrick Pfreundschuh Roland Siegwart Marco Hutter Peyman Moghadam Vaishakh Patil 3DPC 18 5 0 11 Dec 2023
Solving Oscillation Problem in Post-Training Quantization Through a Theoretical Perspective Yuexiao Ma Huixia Li Xiawu Zheng Xuefeng Xiao Rui Wang Shilei Wen Xin Pan Fei Chao Rongrong Ji MQ 10 12 0 21 Mar 2023
Meta Pseudo Labels Hieu H. Pham Zihang Dai Qizhe Xie Minh-Thang Luong Quoc V. Le VLM 253 656 0 23 Mar 2020
What is the State of Neural Network Pruning? Davis W. Blalock Jose Javier Gonzalez Ortiz Jonathan Frankle John Guttag 185 1,027 0 06 Mar 2020
Comparing Rewinding and Fine-tuning in Neural Network Pruning Alex Renda Jonathan Frankle Michael Carbin 222 382 0 05 Mar 2020