3 citations · 8 across the 5 of their papers we have counts for
5 papers
Unlocking the Power of Open Set : A New Perspective for Open-Set Noisy Label Learning
Wenhai Wan, Xinrui Wang, Ming-Kun Xie +3
Learning from noisy data has attracted much attention, where most methods focus on closed-set label noise. However, a more common scenario in the real world is the presence of both…
VI-PINNs: Variance-involved Physics-informed Neural Networks for Fast and Accurate Prediction of Partial Differential Equations
Bin Shan, Ye Li, Shengjun Huang
Although physics-informed neural networks(PINNs) have progressed a lot in many real applications recently, there remains problems to be further studied, such as achieving more accu…
Noise-Robust Bidirectional Learning with Dynamic Sample Reweighting
Chen-Chen Zong, Zheng-Tao Cao, Hong-Tao Guo +4
Deep neural networks trained with standard cross-entropy loss are more prone to memorize noisy labels, which degrades their performance. Negative learning using complementary label…
Meta Objective Guided Disambiguation for Partial Label Learning
Bo-Shi Zou, Ming-Kun Xie, Sheng-Jun Huang
Partial label learning (PLL) is a typical weakly supervised learning framework, where each training instance is associated with a candidate label set, among which only one label is…
A Survey of Learning on Small Data: Generalization, Optimization, and Challenge
Xiaofeng Cao, Weixin Bu, Shengjun Huang +4
Learning on big data brings success for artificial intelligence (AI), but the annotation and training costs are expensive. In future, learning on small data that approximates the g…