Deploying state-of-the-art CNNs requires power-hungry processors and off-chip memory. This precludes the implementation of CNNs in low-power embedded systems. Recent research shows CNNs sustain extreme quantization, binarizing their weights and intermediate feature maps, thereby saving 8-32× memory and collapsing energy-intensive sum-of-products into XNOR-and-popcount operations. We present XNO...

Convolutional neural networks (CNNs) have demonstrated remarkable results in image classification tasks for benchmark and practical uses. The CNNs with deeper architectures have achieved higher performances recently thanks to their robustness to parallel shift of objects in images aw well as their numerous parameters and resulting high expression ability. However, the CNNs have a limited robust...

In this paper, we propose to use Multi-Scale Convolutional Neural Networks (CNNs) to conduct forgery localization in digital image forensics. A unified CNN architecture for input sliding windows of different scales is designed. Then, we elaborately design the training procedures of CNNs on sampled training patches in the IEEE IFS-TC Image Forensics Challenge training images. With a set of caref...

Many state-of-the-art computer vision algorithms use large scale convolutional neural networks (CNNs) as basic building blocks. These CNNs are known for their huge number of parameters, high redundancy in weights, and tremendous computing resource consumptions. This paper presents a learning algorithm to simplify and speed up these CNNs. Specifically, we introduce a “try-and-learn” algorithm to...

Cellular Neural Networks (CNNs) are an evolution of Cellular Automata and Neural Networks and have been traditionally used for pattern recognition. CNNs represent an exciting dimension of complexity in action, whereby the principles of local action, micro-diversity, and massive parallelism have been implemented in a new computing logic and related hardware. This paper introduces the basic featu...

The rise of multi-million-item dataset initiatives has enabled data-hungry machine learning algorithms to reach near-human semantic classification performance at tasks such as visual object and scene recognition. Here we describe the Places Database, a repository of 10 million scene photographs, labeled with scene semantic categories, comprising a large and diverse list of the types of environm...

The uniformly pseudo-projection-anti-monotone (UPPAM) neural network model, which can be considered as the unified continuous-time neural networks (CNNs), includes almost all of the known CNNs individuals. Recently, studies on the critical dynamics behaviors of CNNs have drawn special attentions due to its importance in both theory and applications. In this paper, we will present the analysis o...

Convolutional Neural Networks (CNNs) have achieved state-of-the-art performance when embedded in large vocabulary continuous speech recognition (LVCSR) systems due to its capability of modeling local correlations and reducing translational variations. In all previous related works for ASR, only up to two convolutional layers are employed. In light of the recent success of very deep CNNs in imag...

It has long been recognized that the invariance and equivariance properties of a representation are critically important for success in many vision tasks. In this paper we present Steerable Convolutional Neural Networks, an efficient and flexible class of equivariant convolutional networks. We show that steerable CNNs achieve state of the art results on the CIFAR image classification benchmark....

Convolutional Neural Networks (CNNs) have become the method of choice for learning problems involving 2D planar images. However, a number of problems of recent interest have created a demand for models that can analyze spherical images. Examples include omnidirectional vision for drones, robots, and autonomous cars, molecular regression problems, and global weather and climate modelling. A naiv...