This paper presents a survey of methods for pruning deep neural networks. It begins by
categorising over 150 studies based on the underlying approach used and then focuses on …

The existence of redundancy in Convolutional Neural Networks (CNNs) enables us to
remove some filters/channels with acceptable performance drops. However, the training …

Deep learning has achieved impressive results in many areas, but the deployment of edge
intelligent devices is still very slow. To solve this problem, we propose a novel compression …

Deep learning has achieved impressive results in many areas, but the deployment of edge
intelligent devices is still very slow. To solve this problem, we propose a novel compression …

In an embodiment, a method includes training a neural network model with a first set of
training data. In an embodiment, the method includes calculating divergence for a set of …

Target detection, a crucial component of computer vision, has undergone rapid development
in recent years. While existing target detection networks have demonstrated significant …

Systems, methods, and devices for increasing inference speed of a trained convolutional
neural network (CNN). A first computation speed of first filters having a first filter size in a …