2 citations · 5 across the 5 of their papers we have counts for
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cs.LG2024
E-CGL: An Efficient Continual Graph Learner
Jianhao Guo, Zixuan Ni, Yun Zhu +1
Continual learning has emerged as a crucial paradigm for learning from sequential data while preserving previous knowledge. In the realm of continual graph learning, where graphs c…
cs.LG2023★ 2 cited
Continual Vision-Language Representation Learning with Off-Diagonal Information
Zixuan Ni, Longhui Wei, Siliang Tang +2
Large-scale multi-modal contrastive learning frameworks like CLIP typically require a large amount of image-text samples for training. However, these samples are always collected c…
cs.LG2021★ 2 cited
Self-Supervised Class Incremental Learning
Zixuan Ni, Siliang Tang, Yueting Zhuang
Existing Class Incremental Learning (CIL) methods are based on a supervised classification framework sensitive to data labels. When updating them based on the new class data, they…