22 citations · 52 across the 9 of their papers we have counts for
5 papers · 1 filter
Select-ProtoNet: Learning to Select for Few-Shot Disease Subtype Prediction
Ziyi Yang, Jun Shu, Yong Liang +2
Current machine learning has made great progress on computer vision and many other fields attributed to the large amount of high-quality training samples, while it does not work ve…
Meta Feature Modulator for Long-tailed Recognition
Renzhen Wang, Kaiqin Hu, Yanwen Zhu +3
Deep neural networks often degrade significantly when training data suffer from class imbalance problems. Existing approaches, e.g., re-sampling and re-weighting, commonly address…
Learning to Purify Noisy Labels via Meta Soft Label Corrector
Yichen Wu, Jun Shu, Qi Xie +2
Recent deep neural networks (DNNs) can easily overfit to biased training data with noisy labels. Label correction strategy is commonly used to alleviate this issue by designing a m…
Meta Transition Adaptation for Robust Deep Learning with Noisy Labels
Jun Shu, Qian Zhao, Zongben Xu +1
To discover intrinsic inter-class transition probabilities underlying data, learning with noise transition has become an important approach for robust deep learning on corrupted la…
Learning Adaptive Loss for Robust Learning with Noisy Labels
Jun Shu, Qian Zhao, Keyu Chen +2
Robust loss minimization is an important strategy for handling robust learning issue on noisy labels. Current robust loss functions, however, inevitably involve hyperparameter(s) t…