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Catastrophic forgetting is a notorious issue in deep learning, referring tothe fact that Deep Neural Networks (DNN) could forget the knowledge aboutearlier tasks when learning new tasks. To address this issue, continuallearning has been developed to learn new tasks sequentially and performknowledge transfer from the old tasks to the new ones without forgetting. Whilerecent structure-based learning methods show the capability of alleviating theforgetting problem, these methods start from a redundant full-size network andrequire a complex learning process to gradually grow-and-prune or search thenetwork structure for each task, which is inefficient. To address this problemand enable efficient network expansion for new tasks, we first develop alearnable sparse growth method eliminating the additional pruning/searchingstep in previous structure-based methods. Building on this learnable sparsegrowth method, we then propose GROWN, a novel end-to-end continual learningframework to dynamically grow the model only when necessary. Different from allprevious structure-based methods, GROWN starts from a small seed network,instead of a full-sized one. We validate GROWN on multiple datasets againststate-of-the-art methods, which shows superior performance in both accuracy andmodel size. For example, we achieve 1.0\% accuracy gain on average compared tothe current SOTA results on CIFAR-100 Superclass 20 tasks setting.

GROWN: GRow Only When Necessary for Continual Learning