7 citations · 14 across the 5 of their papers we have counts for
5 papers
Partial-Label Regression
Xin Cheng, Deng-Bao Wang, Lei Feng +2
Partial-label learning is a popular weakly supervised learning setting that allows each training example to be annotated with a set of candidate labels. Previous studies on partial…
A Survey on Extreme Multi-label Learning
Tong Wei, Zhen Mao, Jiang-Xin Shi +2
Multi-label learning has attracted significant attention from both academic and industry field in recent decades. Although existing multi-label learning algorithms achieved good pe…
Instance-Dependent Partial Label Learning
Ning Xu, Congyu Qiao, Xin Geng +1
Partial label learning (PLL) is a typical weakly supervised learning problem, where each training example is associated with a set of candidate labels among which only one is true.…
Prototypical Classifier for Robust Class-Imbalanced Learning
Tong Wei, Jiang-Xin Shi, Yu-Feng Li +1
Deep neural networks have been shown to be very powerful methods for many supervised learning tasks. However, they can also easily overfit to training set biases, i.e., label noise…
Learning from Noisy Labels via Dynamic Loss Thresholding
Hao Yang, Youzhi Jin, Ziyin Li +4
Numerous researches have proved that deep neural networks (DNNs) can fit everything in the end even given data with noisy labels, and result in poor generalization performance. How…