… The success of convolutional neural networks in these situations is limited by how fast we …
FFT based convolution is fast for large filters, but state of the art convolutional neural networks …

… and Convolutional neural network can be applied to practical problems. … simple Convolutional
neural network on image classification. On the basis of the Convolutional neural network, …

… Convolutional neural networks are used to automatically learn a hierarchy of features that
… convolutional neural networks (CNNs) and describe how each parameter affects the network…

… Building deeper and larger convolutional neural networks (CNNs) is a … Modern convolutional
neural networks [31… Among them, state-of-the-art networks such as Xception [3] and …

… convolutional neural network (CNN). A CNN is composed of different types of layers, such as
convolutional … These deep neural networks were also successfully applied to reinforcement …

… (3) We transfer the convolution layers of the proposed networks for fast training and testing
… last convolution layer of SRCNN-Ex with a deconvolution layer, then the whole network will …

… we proposed a recurrent convolutional neural network (RCNN) for (static) object recognition.
The basic idea was to add recurrent connections within every convolutional layer of the feed…

… The fundamental concept of DL comes from artificial neural network (ANN) [2] research, and
the idea … DL and deep convolution neural network (DCNN) have dramatically upgraded the …

… Convolutional neural network (CNN) is a neural network that has outperformed computer …
neural network (ANN). In CNN, information travels unidirectionally as a feed-forward network. …

… But Convolutional Neural Networks (CNN) have provided an … of network specific to a
problem. In this work, we have done a thorough literature survey of Convolutional Neural Networks …