The binary neural network, largely saving the storage and computation, serves as a
promising technique for deploying deep models on resource-limited devices. However, the …

Network quantization is essential for deploying deep models to IoT devices due to its high
efficiency. Most existing quantization approaches rely on the full training datasets and the …

Network binarization emerges as one of the most promising compression approaches
offering extraordinary computation and memory savings by minimizing the bit-width …

Recently, a number of classification techniques have been introduced. However, processing
large dataset in a reasonable time has become a major challenge. This made classification …

Abstract Model binarization is an effective method of compressing neural networks and
accelerating their inference process, which enables state-of-the-art models to run on …

Deep neural networks have shown impressive performance in many areas, including
computer vision and natural language processing. Millions of parameters in deep neural …

Binarization of feature representation is critical for Binarized Neural Networks (BNNs).
Currently, sign function is the commonly used method for feature binarization. Although it …

Deep neural networks have shown great promise in various domains. Meanwhile, problems
including the storage and computing overheads arise along with these breakthroughs. To …

Energy efficiency is critical for running computer vision on battery-powered systems, such as
mobile phones or UAVs (unmanned aerial vehicles, or drones). This book collects the …

Deep neural networks have achieved impressive results in computer vision and machine
learning. Unfortunately, state-of-the-art networks are extremely compute and memory …