PC5-based cellular-V2X evolution and deployment

L Miao, JJ Virtusio, KL Hua - Sensors, 2021 - mdpi.com
C-V2X (Cellular Vehicle-to-Everything) is a state-of-the-art wireless technology used in
autonomous driving and intelligent transportation systems (ITS). This technology has …

Anchor assisted experience replay for online class-incremental learning

H Lin, S Feng, X Li, W Li, Y Ye - IEEE transactions on circuits …, 2022 - ieeexplore.ieee.org
Online class-incremental learning (OCIL) studies the problem of mitigating the phenomenon
of catastrophic forgetting while learning new classes from a continuously non-stationary data …

Curiosity-driven class-incremental learning via adaptive sample selection

Q Hu, Y Gao, B Cao - … Transactions on Circuits and Systems for …, 2022 - ieeexplore.ieee.org
Modern artificial intelligence systems require class-incremental learning while suffering from
catastrophic forgetting in many real-world applications. Due to the missing knowledge of …

Semantic knowledge guided class-incremental learning

S Wang, W Shi, S Dong, X Gao… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Driven by practical needs, research on Class-Incremental Learning (CIL) has received more
and more attentions in recent years. A technical challenge to be conquered by CIL methods …

Infostyler: Disentanglement information bottleneck for artistic style transfer

Y Lyu, Y Jiang, B Peng, J Dong - IEEE Transactions on Circuits …, 2023 - ieeexplore.ieee.org
Artistic style transfer aims to transfer the style of an artwork to a photograph while
maintaining its original overall content. Many prior works focus on designing various transfer …

Expression-tailored talking face generation with adaptive cross-modal weighting

D Zeng, S Zhao, J Zhang, H Liu, K Li - Neurocomputing, 2022 - Elsevier
The key of talking face generation is to synthesize the identity-preserving natural facial
expressions with accurate audio-lip synchronization. To accomplish this, it requires to …

Learning from teacher's failure: A reflective learning paradigm for knowledge distillation

K Xu, L Wang, J Xin, S Li, B Yin - IEEE Transactions on Circuits …, 2023 - ieeexplore.ieee.org
Knowledge Distillation transfers knowledge learned by a teacher network to a student
network. A common mode of knowledge transfer is directly using the teacher network's …

Blind Universal Denoising for Radar Micro-Doppler Spectrograms Using Identical Dual Learning and Reciprocal Adversarial Training

Y Yang, P Wen, W Ye, B Li… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
In practice, radar measurements are hindered by unavoidable noise, which lowers the
signal-to-noise ratio (SNR) and raises the problem of radar signal denoising. Thanks to the …

A Comprehensive Survey of Federated Transfer Learning: Challenges, Methods and Applications

W Guo, F Zhuang, X Zhang, Y Tong, J Dong - arXiv preprint arXiv …, 2024 - arxiv.org
Federated learning (FL) is a novel distributed machine learning paradigm that enables
participants to collaboratively train a centralized model with privacy preservation by …

Fast adapting without forgetting for face recognition

H Liu, X Zhu, Z Lei, D Cao, SZ Li - IEEE Transactions on …, 2020 - ieeexplore.ieee.org
Although face recognition has made dramatic improvements in recent years, there are still
many challenges in real-world applications such as face recognition for the elderly and …