1 code implementation • 20 Jun 2024 • Rushuang Zhou, Zijun Liu, Lei Clifton, David A. Clifton, Kannie W. Y. Chan, Yuan-Ting Zhang, Yining Dong
It enables a robust adaptation of pre-trained models on downstream datasets with limited supervision and high computational efficiency.
no code implementations • 18 Jun 2023 • Rushuang Zhou, Lei Lu, Zijun Liu, Ting Xiang, Zhen Liang, David A. Clifton, Yining Dong, Yuan-Ting Zhang
However, the label scarcity problem, the co-occurrence of multiple CVDs and the poor performance on unseen datasets greatly hinder the widespread application of deep learning-based models.
1 code implementation • 27 Mar 2023 • Rushuang Zhou, Weishan Ye, Zhiguo Zhang, Yanyang Luo, Li Zhang, Linling Li, Gan Huang, Yining Dong, Yuan-Ting Zhang, Zhen Liang
The results show the proposed EEGmatch performs better than the state-of-the-art methods under different incomplete label conditions (with 6. 89% improvement on SEED and 1. 44% improvement on SEED-IV), which demonstrates the effectiveness of the proposed EEGMatch in dealing with the label scarcity problem in emotion recognition using EEG signals.
no code implementations • 29 Jan 2021 • Shane Barratt, Yining Dong, Stephen Boyd
Our focus is on low rank forecasters, which break forecasting up into two steps: estimating a vector that can be interpreted as a latent state, given the past, and then estimating the future values of the time series, given the latent state estimate.