14 citations · 18 across the 3 of their papers we have counts for
3 papers
cs.CV2023★ 1 cited
LA-Net: Landmark-Aware Learning for Reliable Facial Expression Recognition under Label Noise
Zhiyu Wu, Jinshi Cui
Facial expression recognition (FER) remains a challenging task due to the ambiguity of expressions. The derived noisy labels significantly harm the performance in real-world scenar…
cs.LG2022★ 14 cited
Grow and Merge: A Unified Framework for Continuous Categories Discovery
Xinwei Zhang, Jianwen Jiang, Yutong Feng +6
Although a number of studies are devoted to novel category discovery, most of them assume a static setting where both labeled and unlabeled data are given at once for finding new c…
cs.LG2021★ 3 cited
NGC: A Unified Framework for Learning with Open-World Noisy Data
Zhi-Fan Wu, Tong Wei, Jianwen Jiang +3
The existence of noisy data is prevalent in both the training and testing phases of machine learning systems, which inevitably leads to the degradation of model performance. There…