In this paper, we analyze two popular network compression techniques, ie filter pruning and
low-rank decomposition, in a unified sense. By simply changing the way the sparsity …

The success of convolutional neural networks (CNNs) in computer vision applications has
been accompanied by a significant increase of computation and memory costs, which …

Network pruning has been the driving force for the acceleration of neural networks and the
alleviation of model storage/transmission burden. With the advent of AutoML and neural …

We propose a novel regularization-based continual learning method, dubbed as Adaptive
Group Sparsity based Continual Learning (AGS-CL), using two group sparsity-based …

Compressing convolutional neural networks (CNNs) has received ever-increasing research
focus. However, most existing CNN compression methods do not interpret their inherent …

Abstract Deep Neural Networks (DNNs) have achieved extraordinary success in numerous
areas. However, DNNs often carry a large number of weight parameters, leading to the …

Deep neural networks (DNNs) have achieved great success in many object detection tasks.
However, such DNNS-based large object detection models are generally computationally …

Model compression is significant for the wide adoption of Recurrent Neural Networks
(RNNs) in both user devices possessing limited resources and business clusters requiring …

This paper presents an efficient technique to reduce the inference cost of deep and/or wide
convolutional neural network models by pruning redundant features (or filters). Previous …

The model we developed is a novel comprehensive solution to compress and accelerate the
Visual Question Answering systems. In our algorithm Convolutional Neural Network is …