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

Over recent years, there has been a rapid development of deep learning (DL) in both
industry and academia fields. However, finding the optimal hyperparameters of a DL model …

Abstract Continual Semantic Segmentation (CSS) extends static semantic segmentation by
incrementally introducing new classes for training. To alleviate the catastrophic forgetting …

Most existing neural network pruning methods hand-crafted their importance criteria and
structures to prune. This constructs heavy and unintended dependencies on heuristics and …

Zero-shot learning (ZSL) aims to recognize classes that do not have samples in the training
set. One representative solution is to directly learn an embedding function associating visual …

Convolutional neural networks (CNNs) play a crucial role and achieve top results in
computer vision tasks but at the cost of high computational cost and storage complexity. One …

Low-rank compression is an important model compression strategy for obtaining compact
neural network models. In general, because the rank values directly determine the model …

Model compression methods are being developed to bridge the gap between the massive
scale of neural networks and the limited hardware resources on edge devices. Since most …

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 proven to be an effective strategy in model compression. However,
convolutional filter pruning methods usually pay all attention to evaluating filters' importance …