We present a simple but powerful architecture of convolutional neural network, which has a
VGG-like inference-time body composed of nothing but a stack of 3x3 convolution and …

The growing energy and performance costs of deep learning have driven the community to
reduce the size of neural networks by selectively pruning components. Similarly to their …

Channel (or 3D filter) pruning serves as an effective way to accelerate the inference of
neural networks. There has been a flurry of algorithms that try to solve this practical problem …

Abstract As designing appropriate Convolutional Neural Network (CNN) architecture in the
context of a given application usually involves heavy human works or numerous GPU hours …

Filter pruning has been widely used for neural network compression because of its enabled
practical acceleration. To date, most of the existing filter pruning works explore the …

Network compression has been widely studied since it is able to reduce the memory and
computation cost during inference. However, previous methods seldom deal with …