text:   Continual Learning",4,"['Our new Continual Learning work got accepted by #CVPR2022. We introduced a new Bayesian sparse regularization on the network neurons, with novel memory-based full experience reply. \nLucky to work with a great team making this done! An early-version paper: <LINK> <LINK>', 'The sparse regularization encourages to use less model capacity for each task and thus reserves capacity for the subsequent tasks in the continual learning streams. The hierarchical Bayesian modeling optimizes the sparsity.', 'The full experience reply involves the intermediate features for more effective and flexible knowledge sharing and transferring. It also directly guide sparsity modeling at the intermediate layers.', 'The technical details of the sparse regularization are related to our previous work about Bayesian variational dropout: https://t.co/icdIIF90sE']",22,02,817