28 citations · 144 across the 32 of their papers we have counts for
3 papers · 1 filter
Learning Low-Rank Approximation for CNNs
Dongsoo Lee, Se Jung Kwon, Byeongwook Kim +1
Low-rank approximation is an effective model compression technique to not only reduce parameter storage requirements, but to also reduce computations. For convolutional neural netw…
Network Pruning for Low-Rank Binary Indexing
Dongsoo Lee, Se Jung Kwon, Byeongwook Kim +2
Pruning is an efficient model compression technique to remove redundancy in the connectivity of deep neural networks (DNNs). Computations using sparse matrices obtained by pruning…
Structured Compression by Weight Encryption for Unstructured Pruning and Quantization
Se Jung Kwon, Dongsoo Lee, Byeongwook Kim +3
Model compression techniques, such as pruning and quantization, are becoming increasingly important to reduce the memory footprints and the amount of computations. Despite model si…