325 citations · 1.6k across the 109 of their papers we have counts for
17 papers · 1 filter
COVID-MTL: Multitask Learning with Shift3D and Random-weighted Loss for Automated Diagnosis and Severity Assessment of COVID-19
Guoqing Bao, Huai Chen, Tongliang Liu +4
There is an urgent need for automated methods to assist accurate and effective assessment of COVID-19. Radiology and nucleic acid test (NAT) are complementary COVID-19 diagnosis me…
Extended T: Learning with Mixed Closed-set and Open-set Noisy Labels
Xiaobo Xia, Tongliang Liu, Bo Han +4
The label noise transition matrix , reflecting the probabilities that true labels flip into noisy ones, is of vital importance to model label noise and design statistically cons…
A Second-Order Approach to Learning with Instance-Dependent Label Noise
Zhaowei Zhu, Tongliang Liu, Yang Liu
The presence of label noise often misleads the training of deep neural networks. Departing from the recent literature which largely assumes the label noise rate is only determined…
A Survey of Label-noise Representation Learning: Past, Present and Future
Bo Han, Quanming Yao, Tongliang Liu +4
Classical machine learning implicitly assumes that labels of the training data are sampled from a clean distribution, which can be too restrictive for real-world scenarios. However…
Maximum Mean Discrepancy Test is Aware of Adversarial Attacks
Ruize Gao, Feng Liu, Jingfeng Zhang +4
The maximum mean discrepancy (MMD) test could in principle detect any distributional discrepancy between two datasets. However, it has been shown that the MMD test is unaware of ad…
Experimental Quantum Generative Adversarial Networks for Image Generation
He-Liang Huang, Yuxuan Du, Ming Gong +18
Quantum machine learning is expected to be one of the first practical applications of near-term quantum devices. Pioneer theoretical works suggest that quantum generative adversari…