1 citations · 1 across the 5 of their papers we have counts for
5 papers
Elastic Weight Consolidation Done Right for Continual Learning
Xuan Liu, Xiaobin Chang
Weight regularization methods in continual learning (CL) alleviate catastrophic forgetting by assessing and penalizing changes to important model weights. Elastic Weight Consolidat…
LoRA Subtraction for Drift-Resistant Space in Exemplar-Free Continual Learning
Xuan Liu, Xiaobin Chang
In continual learning (CL), catastrophic forgetting often arises due to feature drift. This challenge is particularly prominent in the exemplar-free continual learning (EFCL) setti…
Adaptive Margin Global Classifier for Exemplar-Free Class-Incremental Learning
Zhongren Yao, Xiaobin Chang
Exemplar-free class-incremental learning (EFCIL) presents a significant challenge as the old class samples are absent for new task learning. Due to the severe imbalance between old…
Consistent Prompting for Rehearsal-Free Continual Learning
Zhanxin Gao, Jun Cen, Xiaobin Chang
Continual learning empowers models to adapt autonomously to the ever-changing environment or data streams without forgetting old knowledge. Prompt-based approaches are built on fro…
IIDM: Image-to-Image Diffusion Model for Semantic Image Synthesis
Feng Liu, Xiaobin Chang
Semantic image synthesis aims to generate high-quality images given semantic conditions, i.e. segmentation masks and style reference images. Existing methods widely adopt generativ…