Crowd density estimation has gained significant research interest owing to its potential in
various industries and social applications. Therefore, this article proposes a multistyle joint …

Abstract Knowledge distillation is an attractive approach for learning compact deep neural
networks, which learns a lightweight student model by distilling knowledge from a complex …

The goal of face attribute recognition (FAR) is to recognize the attributes of face images,
such as gender, race, etc. Multi-dataset fusion aims to train a network with multiple datasets …

Quantization-aware training (QAT) and Knowledge Distillation (KD) are combined to achieve
competitive performance in creating low-bit deep learning models. However, existing works …

Abstract Knowledge distillation is one of the most persuasive approaches to model
compression that transfers the representational expertise from large deep-learning teacher …

In recent years, various distillation methods for semantic segmentation have been proposed.
However, these methods typically train the student model to imitate the intermediate features …

Accurate sleep staging is crucial for the diagnosis of diseases such as sleep disorders.
Existing sleep staging models with excellent performance are usually large and require a lot …

The increasing global demand for skin disease diagnostics emphasizes the urgent need for
advancements in AI-assisted diagnostic technologies for dermatoscopic images. In current …

Researchers have resorted to model quantization to compress and accelerate graph neural
networks (GNNs). Nevertheless, several challenges remain:(1) quantization functions …

This work presents a Fully BInarized Large Language Model (FBI-LLM), demonstrating for
the first time how to train a large-scale binary language model from scratch (not the partial …