Federated learning (FL) is a promising approach for training decentralized data located on
local client devices while improving efficiency and privacy. However, the distribution and …

The prosperity of deep neural networks (DNNs) is largely benefited from open-source
datasets, based on which users can evaluate and improve their methods. In this paper, we …

As the scientific and technological achievements produced by human intellectual labor and
computation cost, model intellectual property (IP) protection, which refers to preventing the …

In real-world applications, deep learning models often run in non-stationary environments
where the target data distribution continually shifts over time. There have been numerous …

Human action recognition which recognizes human actions in a video is a fundamental task
in computer vision field. Although multiple existing methods with single-view or multi-view …

This paper focuses on the data-insufficiency problem in multi-task learning within an
episodic training setup. Specifically, we explore the potential of heterogeneous information …

Stuttering affects almost 1\% of the world's population. It has a deep sociological impact and
hinders the people who stutter from taking advantage of voice-assisted services. Automatic …

In a privacy-focused era, Federated Learning (FL) has emerged as a promising machine
learning technique. However, most existing FL studies assume that the data distribution …

Accurate three-dimensional mapping and digital twinning provides a powerful tool for
effective maintenance of civil infrastructure and supports efficient future planning of new …

This paper targets at a new and challenging setting of knowledge transfer from multiple
source domains to a single target domain, where target data is few shot or even one shot …