2 citations · 5 across the 5 of their papers we have counts for
5 papers
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…
Degeneration-Tuning: Using Scrambled Grid shield Unwanted Concepts from Stable Diffusion
Zixuan Ni, Longhui Wei, Jiacheng Li +3
Owing to the unrestricted nature of the content in the training data, large text-to-image diffusion models, such as Stable Diffusion (SD), are capable of generating images with pot…
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…
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…
Revisiting Catastrophic Forgetting in Class Incremental Learning
Zixuan Ni, Haizhou Shi, Siliang Tang +3
Although the concept of catastrophic forgetting is straightforward, there is a lack of study on its causes. In this paper, we systematically explore and reveal three causes for cat…