17 citations · 25 across the 6 of their papers we have counts for
4 papers · 1 filter
Uncertainty-inspired Open Set Learning for Retinal Anomaly Identification
Meng Wang, Tian Lin, Lianyu Wang +21
Failure to recognize samples from the classes unseen during training is a major limitation of artificial intelligence in the real-world implementation for recognition and classific…
Complementary Labels Learning with Augmented Classes
Zhongnian Li, Jian Zhang, Mengting Xu +2
Complementary Labels Learning (CLL) arises in many real-world tasks such as private questions classification and online learning, which aims to alleviate the annotation cost compar…
Learning from Positive and Unlabeled Data with Augmented Classes
Zhongnian Li, Liutao Yang, Zhongchen Ma +3
Positive Unlabeled (PU) learning aims to learn a binary classifier from only positive and unlabeled data, which is utilized in many real-world scenarios. However, existing PU learn…
InfoAT: Improving Adversarial Training Using the Information Bottleneck Principle
Mengting Xu, Tao Zhang, Zhongnian Li +1
Adversarial training (AT) has shown excellent high performance in defending against adversarial examples. Recent studies demonstrate that examples are not equally important to the…