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 …

In this paper, we present CORING, which is short for effiCient tensOr decomposition-based
filteR prunING, a novel filter pruning methodology for neural networks. CORING is crafted to …

Network compression techniques that combine tensor decompositions and pruning have
shown promise in leveraging the advantages of both strategies. In this work, we propose …

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

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

Existing methods for filter pruning mostly rely on specific data-driven paradigms but lack the
interpretability. Besides, these approaches usually assign layer-wise compression ratios …

Deep neural networks (DNNs) have been a pivotal technology in a myriad of fields, boasting
remarkable achievements. Nevertheless, their substantial workload and inherent …

Over the past few years, larger and deeper neural network models, particularly convolutional
neural networks (CNNs), have consistently advanced state-of-the-art performance across …

Deep convolutional neural networks (CNNs) have exhibited exceptional performance in a
range of computer vision tasks. However, these deep CNNs typically demand significant …