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 …

The large pre-trained BERT has achieved remarkable performance on Natural Language
Processing (NLP) tasks but is also computation and memory expensive. As one of the …

This paper studies the Binary Neural Networks (BNNs) in which weights and activations are
both binarized into 1-bit values, thus greatly reducing the memory usage and computational …

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

Real-time video matting on edge devices faces significant computational resource
constraints, limiting the widespread use of video matting in applications such as online …

This paper explores network binarization, a radical form of quantization, compressing model
weights to a single bit, specifically for Large Language Models (LLMs) compression. Due to …

Abstract Binary Neural Networks (BNNs) show great promise for real-world embedded
devices. As one of the critical steps to achieve a powerful BNN, the scale factor calculation …

Deepfake detection aims to contrast the spread of deep-generated media that undermines
trust in online content. While existing methods focus on large and complex models the need …

Recently, there have been tremendous efforts in developing lightweight Deep Neural
Networks (DNNs) with satisfactory accuracy, which can enable the ubiquitous deployment of …