  We propose a novel regularization-based continual learning method, dubbed as
Adaptive Group Sparsity based Continual Learning (AGS-CL), using two group
sparsity-based penalties. Our method selectively employs the two penalties when
learning each node based its the importance, which is adaptively updated after
learning each new task. By utilizing the proximal gradient descent method for
learning, the exact sparsity and freezing of the model is guaranteed, and thus,
the learner can explicitly control the model capacity as the learning
continues. Furthermore, as a critical detail, we re-initialize the weights
associated with unimportant nodes after learning each task in order to prevent
the negative transfer that causes the catastrophic forgetting and facilitate
efficient learning of new tasks. Throughout the extensive experimental results,
we show that our AGS-CL uses much less additional memory space for storing the
regularization parameters, and it significantly outperforms several
state-of-the-art baselines on representative continual learning benchmarks for
both supervised and reinforcement learning tasks.
