21 citations · 28 across the 30 of their papers we have counts for
3 papers · 1 filter
Group Benefits Instances Selection for Data Purification
Zhenhuang Cai, Chuanyi Zhang, Dan Huang +3
Manually annotating datasets for training deep models is very labor-intensive and time-consuming. To overcome such inferiority, directly leveraging web images to conduct training d…
Learning with Imbalanced Noisy Data by Preventing Bias in Sample Selection
Huafeng Liu, Mengmeng Sheng, Zeren Sun +3
Learning with noisy labels has gained increasing attention because the inevitable imperfect labels in real-world scenarios can substantially hurt the deep model performance. Recent…
Adaptive Integration of Partial Label Learning and Negative Learning for Enhanced Noisy Label Learning
Mengmeng Sheng, Zeren Sun, Zhenhuang Cai +3
There has been significant attention devoted to the effectiveness of various domains, such as semi-supervised learning, contrastive learning, and meta-learning, in enhancing the pe…