435 citations · 503 across the 62 of their papers we have counts for
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cs.LG2021★ 1 cited
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.…
cs.CV2021★ 3 cited
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…
cs.LG2021
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…