5 citations · 9 across the 24 of their papers we have counts for
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cs.LG2024
Online Policy Distillation with Decision-Attention
Xinqiang Yu, Chuanguang Yang, Chengqing Yu +3
Policy Distillation (PD) has become an effective method to improve deep reinforcement learning tasks. The core idea of PD is to distill policy knowledge from a teacher agent to a s…
cs.LG2023
Low-redundancy Distillation for Continual Learning
RuiQi Liu, Boyu Diao, Libo Huang +4
Continual learning (CL) aims to learn new tasks without erasing previous knowledge. However, current CL methods primarily emphasize improving accuracy while often neglecting traini…
cs.LG2022★ 2 cited
Lifelong Generative Learning via Knowledge Reconstruction
Libo Huang, Zhulin An, Xiang Zhi +1
Generative models often incur the catastrophic forgetting problem when they are used to sequentially learning multiple tasks, i.e., lifelong generative learning. Although there are…