Self-Attention Meta-Learner for Continual Learning

Continual learning aims to provide intelligent agents capable of learning multiple tasks sequentially with neural networks. In most settings of the current approaches, the agent starts from randomly initialized parameters and is optimized to master the current task regardless of the usefulness of the learned representation for future tasks. Moreover, each of the future tasks uses all the previously learned knowledge although parts of this knowledge might not be helpful for its learning. These cause interference among tasks, especially when the data of previous tasks is not accessible. In this paper, we propose a new method, named Self-Attention Meta-Learner (SAM), which learns a prior knowledge for continual learning that permits learning a sequence of tasks, while avoiding catastrophic forgetting. SAM incorporates an attention mechanism that learns to select the particular relevant representation for each future task. We empirically show that we can achieve a better performance than several state-of-the-art methods for continual learning by building on top of the selected representation learned by SAM. We also show the role of the meta-attention mechanism in boosting informative features corresponding to the input task and identifying the correct target in the task agnostic inference. Finally, we demonstrate that popular existing continual learning methods gain a performance boost when they adopt SAM as a starting point.