Deep learning (DL) has recently changed the development of intelligent systems and is
widely adopted in many real-life applications. Despite their various benefits and potentials …

This paper presents an extensive literature review on Binary Neural Network (BNN). BNN
utilizes binary weights and activation function parameters to substitute the full-precision …

This chapter provides approaches to the problem of quantizing the numerical values in deep
Neural Network computations, covering the advantages/disadvantages of current methods …

Deep learning models are vulnerable to adversarial examples. As a more threatening type
for practical deep learning systems, physical adversarial examples have received extensive …

Prohibited items detection in X-ray images often plays an important role in protecting public
safety, which often deals with color-monotonous and luster-insufficient objects, resulting in …

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

Guided depth map super-resolution (GDSR), as a hot topic in multi-modal image processing,
aims to upsample low-resolution (LR) depth maps with additional information involved in …

Quantization has emerged as one of the most prevalent approaches to compress and
accelerate neural networks. Recently, data-free quantization has been widely studied as a …

Although deep learning has received extensive attention and achieved excellent
performance in various scenarios, it suffers from adversarial examples to some extent. In …

Neural network quantization aims at reducing bit-widths of weights and activations for
memory and computational efficiency. Since a linear quantizer (ie, round (*) function) cannot …