A Appendix

A.1 PAC Bayesian Bound In this part, we provide a detailed P AC-Bound based on the continual learning scenario. We now consider the bound in the continual learning scenario. Based on Eq. (6), the expected error of C.1 Pseudo-code for FS-ER Comparing the pseudo-code of V anilla ER, FS-ER only adds the adversarial weight perturbation v . D.1 Datasets Table 4 summarizes the statistics of four datasets used in our experiments. Both fully connected layers have 2048 units in each layer.