Winner of the 1st Learning and Mining with Noisy Labels Challenge (link) in IJCAI-ECAI 2022.
Ranks:
- 1st under Task1 (Image Classification, CIFAR-10N);
- 1st under Task1 (Image Classification, CIFAR-100N);
- 1st under Task2 (Label Noise Detection, CIFAR-10N);
- 1st under Task2 (Label Noise Detection, CIFAR-100N);
The technical report of our work can be found here.
Title: ProMix: Combating Label Noise via Maximizing Clean Sample Utility
Authors: Haobo Wang*, Ruixuan Xiao*, Yiwen Dong, Lei Feng, Junbo Zhao
Institute: Zhejiang University, Chongqing Unveristy
The shell codes are provided in run.sh
. Please correctly set the data path arguments.
Accuracy comparisons on CIFAR-10N and CIFAR-100N under different noise types.
Detection ability of ProMix on CIFAR-10N datasets. Noise detection indicates the wrongly-labeled data are regarded as positive, and clean detection regards correctly-labeled ones as positive.