Task-Free Continual Learning via Online Discrepancy Distance Learning Fei Y e and Adrian G. Bors Department of Computer Science University of York York, YO10 5GH, UK {fy689,adrian.bors }@york.ac.uk

TFCL, these methods lack theoretical guarantees. Moreover, there are no theoretical studies about forgetting during TFCL. This paper develops a new theoretical analysis framework that derives generalization bounds based on the discrepancy distance between the visited samples and the entire information made available for training the model.