12 citations · 12 across the 2 of their papers we have counts for
2 papers
cs.CV2024
REPrune: Channel Pruning via Kernel Representative Selection
Mincheol Park, Dongjin Kim, Cheonjun Park +4
Channel pruning is widely accepted to accelerate modern convolutional neural networks (CNNs). The resulting pruned model benefits from its immediate deployment on general-purpose s…
cs.CV2020★ 12 cited
Neuron Merging: Compensating for Pruned Neurons
Woojeong Kim, Suhyun Kim, Mincheol Park +1
Network pruning is widely used to lighten and accelerate neural network models. Structured network pruning discards the whole neuron or filter, leading to accuracy loss. In this wo…