Abstract: In recent years, Convolutional Neural Network (CNN) has made a series of
breakthrough research results in the fields of image classification, object detection, semantic …

… In this paper, we show that further error rate reduction can be obtained by using convolutional
neural networks (CNNs). We first present a concise description of the basic CNN and …

… There are a number of reasons that convolutional neural networks are … neural network
whose performance would be same as a CNN. But in practical training, a standard neural network …

… Convolutional neural networks (CNNs) have been successfully applied to many tasks such
as digit and object recognition. Using convolutional (… tiled convolution neural networks (Tiled …

… Let us assume that for some layer of our convolutional neural network we seek to learn filters
of size H × W. To do this, we parametrize each filter f ∈ CH×W in our network directly in the …

… Second, we used convolutional neural networks. The elastic … found that the convolutional
neural network performs the best … by convolutional neural networks [3], while standard neural …

… , we initialize a convolutional neural network with another convolutional neural network (that …
, a deep autoencoder or a deep belief network. This method can sometimes workes very …

… Deep Convolutional Neural Network (CNN) is a special type of Neural Networks, which
has shown exemplary performance on several competitions related to Computer Vision and …

… The deep neural network has different variants to deal with the … convolutional neural networks
(CNNs) and generative adversarial networks (GANs) for images, recurrent neural network (…

In the last few years, convolutional neural networks (CNNs) convolutional neural networks (CNNs)
, along with recurrent neural networks (RNNs), have become a basic building block in …