Learning with noisy labels for robust fatigue detection
M Wang, R Hu, X Zhu, D Zhu, X Wang - Knowledge-Based Systems, 2024 - Elsevier
Fatigue is a significant safety concern across various domains, and accurate detection is
vital. However, the commonly employed fine-grained labels (seconds-based) frequently …
vital. However, the commonly employed fine-grained labels (seconds-based) frequently …
Underwater Object Detection in the Era of Artificial Intelligence: Current, Challenge, and Future
Underwater object detection (UOD), aiming to identify and localise the objects in underwater
images or videos, presents significant challenges due to the optical distortion, water …
images or videos, presents significant challenges due to the optical distortion, water …
Temporal-Noise-Aware Neural Networks for Suicidal Ideation Prediction Using Physiological Data
The robust generalization of deep learning models in the presence of inherent noise
remains a significant challenge, especially when labels are ambiguous due to their …
remains a significant challenge, especially when labels are ambiguous due to their …
[HTML][HTML] Multi-Task Diffusion Learning for Time Series Classification
Current deep learning models for time series often face challenges with generalizability in
scenarios characterized by limited samples or inadequately labeled data. By tapping into the …
scenarios characterized by limited samples or inadequately labeled data. By tapping into the …
EEG-MACS: Manifold Attention and Confidence Stratification for EEG-based Cross-Center Brain Disease Diagnosis under Unreliable Annotations
Z Song, R Qin, H Ren, Z Liang, Y Guo… - Proceedings of the …, 2024 - dl.acm.org
Cross-center data heterogeneity and annotation unreliability significantly challenge the
intelligent diagnosis of diseases using brain signals. A notable example is the EEG-based …
intelligent diagnosis of diseases using brain signals. A notable example is the EEG-based …
EEG-ReMinD: Enhancing Neurodegenerative EEG Decoding through Self-Supervised State Reconstruction-Primed Riemannian Dynamics
Z Wang, Z Song, Y Guo, Y Liu, G Xu, M Zhang… - arXiv preprint arXiv …, 2025 - arxiv.org
The development of EEG decoding algorithms confronts challenges such as data sparsity,
subject variability, and the need for precise annotations, all of which are vital for advancing …
subject variability, and the need for precise annotations, all of which are vital for advancing …
Mining Irregular Time Series Data with Noisy Labels: A Risk Estimation Approach
Time series data are widely used in critical sectors such as finance, healthcare, and
environment to analyze temporal trends and patterns for prediction, monitoring, and decision …
environment to analyze temporal trends and patterns for prediction, monitoring, and decision …
Mining Irregular Time Series Data with Noisy Labels: A Risk Estimation
Time series data are widely used in critical sectors such as finance, healthcare, and
environment to analyze temporal trends and patterns for prediction, monitoring, and decision …
environment to analyze temporal trends and patterns for prediction, monitoring, and decision …