Partial-Label Regression

1 code implementation • AAAI 2023 • Xin Cheng, Deng-Bao Wang, Lei Feng, Min-Ling Zhang, Bo An

Our proposed methods are theoretically grounded and can be compatible with any models, optimizers, and losses.

Partial Label Learning regression +1

On the Pitfall of Mixup for Uncertainty Calibration

1 code implementation • CVPR 2023 • Deng-Bao Wang, Lanqing Li, Peilin Zhao, Pheng-Ann Heng, Min-Ling Zhang

It has been recently found that models trained with mixup also perform well on uncertainty calibration.

Rethinking Calibration of Deep Neural Networks: Do Not Be Afraid of Overconfidence

no code implementations • NeurIPS 2021 • Deng-Bao Wang, Lei Feng, Min-Ling Zhang

Capturing accurate uncertainty quantification of the prediction from deep neural networks is important in many real-world decision-making applications.

Decision Making Uncertainty Quantification

Learning from Noisy Labels via Dynamic Loss Thresholding

no code implementations • 1 Apr 2021 • Hao Yang, Youzhi Jin, Ziyin Li, Deng-Bao Wang, Lei Miao, Xin Geng, Min-Ling Zhang

During the training process, DLT records the loss value of each sample and calculates dynamic loss thresholds.