55 citations · 73 across the 8 of their papers we have counts for
4 papers · 1 filter
Learning to Re-weight Examples with Optimal Transport for Imbalanced Classification
Dandan Guo, Zhuo Li, Meixi Zheng +3
Imbalanced data pose challenges for deep learning based classification models. One of the most widely-used approaches for tackling imbalanced data is re-weighting, where training s…
Overcoming Long-term Catastrophic Forgetting through Adversarial Neural Pruning and Synaptic Consolidation
Jian Peng, Bo Tang, Hao Jiang +4
Artificial neural networks face the well-known problem of catastrophic forgetting. What's worse, the degradation of previously learned skills becomes more severe as the task sequen…
Understanding the Importance of Single Directions via Representative Substitution
Li Chen, Hailun Ding, Qi Li +3
Understanding the internal representations of deep neural networks (DNNs) is crucal to explain their behavior. The interpretation of individual units, which are neurons in MLPs or…
Overcoming Catastrophic Forgetting by Soft Parameter Pruning
Jian Peng, Jiang Hao, Zhuo Li +5
Catastrophic forgetting is a challenge issue in continual learning when a deep neural network forgets the knowledge acquired from the former task after learning on subsequent tasks…