10 citations · 26 across the 20 of their papers we have counts for
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cs.LG2023
Online Continual Learning via Logit Adjusted Softmax
Zhehao Huang, Tao Li, Chenhe Yuan +2
Online continual learning is a challenging problem where models must learn from a non-stationary data stream while avoiding catastrophic forgetting. Inter-class imbalance during tr…
cs.CV2023
Low-Dimensional Gradient Helps Out-of-Distribution Detection
Yingwen Wu, Tao Li, Xinwen Cheng +2
Detecting out-of-distribution (OOD) samples is essential for ensuring the reliability of deep neural networks (DNNs) in real-world scenarios. While previous research has predominan…
cs.LG2023★ 2 cited
Investigating Catastrophic Overfitting in Fast Adversarial Training: A Self-fitting Perspective
Zhengbao He, Tao Li, Sizhe Chen +1
Although fast adversarial training provides an efficient approach for building robust networks, it may suffer from a serious problem known as catastrophic overfitting (CO), where m…