Convolutional neural networks have recently been shown to outperform fully connected deep neural networks on several speech recognition tasks. Their superior performance is due to their convolutional structure that processes several, slightly shifted versions of the input window using the same weights, and then pools the resulting neural activations. This pooling operation makes the network les...

Convolutional neural networks provide an eecient method to constrain the complexity of feedforward neural networks by weightsharing. This network topology has been applied in particular to image classiication when raw images are to be classi-ed without preprocessing. In this paper two variations of convolutional networks-Neocognitron and Neoperceptron-are compared with classiiers based on fully...

Traffic sign detection is an important task in traffic sign recognition systems. Chinese traffic signs have their unique features compared with traffic signs of other countries. Convolutional neural networks (CNNs) have achieved a breakthrough in computer vision tasks and made great success in traffic sign classification. In this paper, we present a Chinese traffic sign detection algorithm base...

Filters in a convolutional network are typically parametrized in a pixel basis. As an orthonormal basis, pixels may represent any arbitrary vector in R. In this paper, we relax this orthonormality requirement and extend the set of viable bases to the generalized notion of frames. When applying suitable frame bases to ResNets on Cifar-10+ we demonstrate improved error rates by substitution only....

Convolutional Neural Network (CNN) was firstly introduced in Computer Vision for image recognition by LeCun et al. in 1989. Since then, it has been widely used in image recognition and classification tasks. The recent impressive success of Krizhevsky et al. in ILSVRC 2012 competition demonstrates the significant advance of modern deep CNN on image classification task. Inspired by his work, many...

The game of Go is more challenging than other board games, due to the difficulty of constructing a position or move evaluation function. In this paper we investigate whether deep convolutional networks can be used to directly represent and learn this knowledge. We train a large 12-layer convolutional neural network by supervised learning from a database of human professional games. The network ...

09.15 10.45 Paper Session I o Exploiting the PANORAMA Representation for Convolutional Neural Network Classification and Retrieval Konstantinos Sfikas, Theoharis Theoharis and Ioannis Pratikakis o LightNet: A Lightweight 3D Convolutional Neural Network for Real-Time 3D Object Recognition Shuaifeng Zhi, Yongxiang Liu, Xiang Li and Yulan Guo o Unstructured point cloud semantic labeling using deep...

A deep network structure is formed with LSTM layer and convolutional layer interweaves with each other. The Layerwise Interweaving Convolutional LSTM(LIC-LSTM) enhanced the feature extraction ability of LSTM stack and is capable for versatile sequential data modeling. Its unique network structure allows it to extract higher level features with sequential information involved. Experiment results...