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

By forcing N out of M consecutive weights to be non-zero, the recent N: M fine-grained
network sparsity has received increasing attention with its two attractive advantages over …

Deep learning technology has found a promising application in lightweight model design, for
which pruning is an effective means of achieving a large reduction in both model parameters …

Abstract Visual Pattern Recognition Networks (VPRNs) are widely used in various visual
data based applications such as computer vision and edge AI. VPRNs help to enhance a …

Observing that the existing model compression approaches only focus on reducing the
redundancies in convolutional neural networks (CNNs) along one particular dimension (eg …

The large data scale and computational resources required by Convolutional Neural
Networks (CNNs) hinder the practical application on mobile devices. However, channel …

Neurologically, filter pruning is a procedure of forgetting and remembering recovering.
Prevailing methods directly forget less important information from an unrobust baseline at …

Abstract Deep Neural Networks (DNNs) enjoy the welfare of convolution, while also bearing
huge computational pressure. Therefore, model compression techniques are used to …

The success of CNNs for various vision tasks has been accompanied by a significant
increase in required FLOPs and parameter quantities, which has impeded the deployment of …

Sparsification and low-rank decomposition are two important techniques to compress deep
neural network (DNN) models. To date, these two popular yet distinct approaches are …