The remarkable performance of deep Convolutional neural networks (CNNs) is generally
attributed to their deeper and wider architectures, which can come with significant …

Recent years have witnessed an exponential increase in the use of mobile and embedded
devices. With the great success of deep learning in many fields, there is an emerging trend …

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 …

Attention mechanism, especially channel attention, has gained great success in the
computer vision field. Many works focus on how to design efficient channel attention …

In the 1940s, Claude Shannon developed the information theory focusing on quantifying the
maximum data rate that can be supported by a communication channel. Guided by this …

Convolutional neural network (CNN) pruning has become one of the most successful
network compression approaches in recent years. Existing works on network pruning …

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 …

Most state-of-the-art action localization systems process each action proposal individually,
without explicitly exploiting their relations during learning. However, the relations between …

Previous works utilized" smaller-norm-less-important" criterion to prune filters with smaller
norm values in a convolutional neural network. In this paper, we analyze this norm-based …

Structured pruning of filters or neurons has received increased focus for compressing
convolutional neural networks. Most existing methods rely on multi-stage optimizations in a …