Deep neural models, in recent years, have been successful in almost every field, even
solving the most complex problem statements. However, these models are huge in size with …

摘要高性能的深度学习网络通常是计算型和参数密集型的, 难以应用于资源受限的边缘设备.
为了能够在低资源设备上运行深度学习模型, 需要研发高效的小规模网络 …

In recent years, deep neural networks have been successful in both industry and academia,
especially for computer vision tasks. The great success of deep learning is mainly due to its …

Scaling up the convolutional neural network (CNN) size (eg, width, depth, etc.) is known to
effectively improve model accuracy. However, the large model size impedes training on …

We propose ResRep, a novel method for lossless channel pruning (aka filter pruning), which
slims down a CNN by reducing the width (number of output channels) of convolutional …

Abstract Knowledge distillation (KD) is an effective learning paradigm for improving the
performance of lightweight student networks by utilizing additional supervision knowledge …

Modern efficient Convolutional Neural Networks (CNNs) are able to perform semantic
segmentation both swiftly and accurately, which covers typically separate detection tasks …

Deep convoloutional networks have been widely deployed in modern cyber-physical
systems performing different visual classification tasks. As the fog and edge devices have …

Text recognition remains a fundamental and extensively researched topic in computer
vision, largely owing to its wide array of commercial applications. The challenging nature of …

Abstract Knowledge distillation which learns a lightweight student model by distilling
knowledge from a cumbersome teacher model is an attractive approach for learning …