Article history: Received 27 April 2015 Received in revised form 8 June 2015 Accepted 8 June 2015 Available online 16 June 2015

Deep convolutional neural networks are generally regarded as robust function approximators. So far, this intuition is based on perturbations to external stimuli such as the images to be classified. Here we explore the robustness of convolutional neural networks to perturbations to the internal weights and architecture of the network itself. We show that convolutional networks are surprisingly r...

Exploring the Design Space of Deep Convolutional Neural Networks at Large Scale

In the past three decades, the use of smart methods in medical diagnostic systems has attractedthe attention of many researchers. However, no smart activity has been provided in the eld ofmedical image processing for diagnosis of bladder cancer through cystoscopy images despite the highprevalence in the world. In this paper, two well-known convolutional neural networks (CNNs) ...

Convolutional neural networks have achieved a great success in the recent years. Although, the way to maximize the performance of the convolutional neural networks still in the beginning. Furthermore, the optimization of the size and the time that need to train the convolutional neural networks is very far away from reaching the researcher's ambition. In this paper, we proposed a new convolutio...

In deep neural networks with convolutional layers, each layer typically has fixed-size/single-resolution receptive field (RF). Convolutional layers with a large RF capture global information from the input features, while layers with small RF size capture local details with high resolution from the input features. In this work, we introduce novel deep multi-resolution fully convolutional neural...

It is well accepted that convolutional neural networks play an important role in learning excellent features for image classification and recognition. However, in tradition they only allow adjacent layers connected, limiting integration of multi-scale information. To further improve their performance, we present a concatenating framework of shortcut convolutional neural networks. This framework...

Use of neural networks for computer vision, speech recognition, and other applications has exploded in recent years, in part due to their unprecedented performance on a variety of benchmarks. Nonetheless, highthroughput and energy-efficient evaluation of such neural networks, and in particular, convolutional neural networks (CNNs), remains an active field of research. Evaluation of networks is ...

Convolutional neural networks (CNN) have recently seen tremendous success in various computer vision tasks. However, their application to problems with high dimensional input and output, such as high-resolution image video segmentation or 3D medical imaging, has been limited by factors. Primarily, the training stage, it is necessary store network activations for back-propagation. In these setti...