… Section VI concludes the survey by giving a bird’s eye … convolution neural networks cannot
learn data in non-Euclidean space, the previous GNN model cannot combine with convolution…

… On the other hand, in this survey, we discuss the intrinsic … The various CNN architectures
discussed in this survey are broadly … This survey also gives an insight into the basic structure of …

… the development history of convolutional neural networks and the advantages of
conventional neural networks. The structure of the convolutional neural network is highlighted. …

… the efforts of visualizing and understanding convolutional neural networks. In Section 9, we
will … In Section 10, we will discuss recent efforts of combining deep neural networks to model …

… In the recent era, the success of deep convolutional neural networks (CNN) has influenced
the field of segmentation greatly and gave us various successful models to date. In this survey…

… performance of deep Convolutional neural networks (CNNs) is … Pruning neural networks
has thus gained interest since it … This article surveys the recent progress towards structured …

… processor units, the research on convolutional neural networks has been emerged swiftly
and … , we provide a broad survey of the recent advances in convolutional neural networks. We …

… During the last decade, Convolutional Neural Networks (CNNs) have become the de facto
… CNNs are feed-forward Artificial Neural Networks (ANNs) with alternating convolutional and …

Over the past few years, Convolutional neural networks (ConvNets) is emerging as computer
vision discipline within deep learning. ConvNets is a key strategy for addressing computer …

… Nowadays, the Convolutional Neural Networks (CNNs) have achieved impressive
performance on many computer vision related tasks, such as object detection, image recognition, …