Task-Free Continual Learning via Online Discrepancy Distance Learning. (arXiv:2210.06579v1 [cs.CV])

Learning from non-stationary data streams, also called Task-Free Continual
Learning (TFCL) remains challenging due to the absence of explicit task
information. Although recently some methods have been proposed for TFCL, they
lack theoretical guarantees. Moreover, forgetting analysis during TFCL was not
studied theoretically before. This paper develops a new theoretical analysis
framework which provides generalization bounds based on the discrepancy
distance between the visited samples and the entire information made available
for training the model. This analysis gives new insights …

arxiv continual distance learning free