298 citations · 1.5k across the 72 of their papers we have counts for
4 papers · 1 filter
On the Dynamics Under the Unhinged Loss and Beyond
Xiong Zhou, Xianming Liu, Hanzhang Wang +3
Recent works have studied implicit biases in deep learning, especially the behavior of last-layer features and classifier weights. However, they usually need to simplify the interm…
Prototype-Anchored Learning for Learning with Imperfect Annotations
Xiong Zhou, Xianming Liu, Deming Zhai +3
The success of deep neural networks greatly relies on the availability of large amounts of high-quality annotated data, which however are difficult or expensive to obtain. The resu…
Learning with Noisy Labels via Sparse Regularization
Xiong Zhou, Xianming Liu, Chenyang Wang +3
Learning with noisy labels is an important and challenging task for training accurate deep neural networks. Some commonly-used loss functions, such as Cross Entropy (CE), suffer fr…
Asymmetric Loss Functions for Learning with Noisy Labels
Xiong Zhou, Xianming Liu, Junjun Jiang +2
Robust loss functions are essential for training deep neural networks with better generalization power in the presence of noisy labels. Symmetric loss functions are confirmed to be…