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

Artificial perception for robots operating in outdoor natural environments, including forest
scenarios, has been the object of a substantial amount of research for decades. Regardless …

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 …

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

Filter pruning is advocated for accelerating deep neural networks without dedicated
hardware or libraries, while maintaining high prediction accuracy. Several works have cast …

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

Automatic modulation recognition (AMR) plays an important role in communication system.
With the expansion of data volume and the development of computing power, deep learning …

The enormous data and computational resources required by Convolutional Neural
Networks (CNNs) hinder the practical application on mobile devices. To solve this restrictive …

Abstract Knowledge distillation guides student networks' training and enhances their
performance through excellent teacher networks. However, along with the performance …

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 …