CVPR 2023 - Exploring Data Geometry for Continual Learning

In this episode we discuss Exploring Data Geometry for Continual Learning by Zhi Gao, Chen Xu, Feng Li, Yunde Jia, Mehrtash Harandi, Yuwei Wu. The paper explores the concept of continual learning, which involves effectively learning from a constantly changing stream of data without forgetting the knowledge gained from the old data. The study analyzes how data geometry can be used to achieve this goal, especially for non-Euclidean data structures that cannot be captured using Euclidean space. The proposed method involves dynamically expanding the geometry of the underlying space to account for new data and preserving old data's geometric structures. The approach achieves better performance than traditional Euclidean-based methods.