  In this paper, we propose a new method to overcome catastrophic forgetting by
adding generative regularization to Bayesian inference framework. Bayesian
method provides a general framework for continual learning. We could further
construct a generative regularization term for all given classification models
by leveraging energy-based models and Langevin-dynamic sampling to enrich the
features learned in each task. By combining discriminative and generative loss
together, we empirically show that the proposed method outperforms
state-of-the-art methods on a variety of tasks, avoiding catastrophic
forgetting in continual learning. In particular, the proposed method
outperforms baseline methods over 15% on the Fashion-MNIST dataset and 10% on
the CUB dataset
