Most existing methods that cope with noisy labels usually assume that the class-wise data distributions are well balanced. They are difficult to deal with the practical scenarios where training samples have imbalanced distributions, since they are …
Open-set semi-supervised learning~(open-set SSL) investigates a challenging but practical scenario where out-of-distribution (OOD) samples are contained in the unlabeled data. While the mainstream technique seeks to completely filter out the OOD …