  Continual learning aims to provide intelligent agents capable of learning
multiple tasks sequentially with neural networks. One of its main challenging,
catastrophic forgetting, is caused by the neural networks non-optimal ability
to learn in non-stationary distributions. 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. Each task builds a specific representation branch on top of the
selected knowledge, avoiding the interference between tasks. We evaluate the
proposed method on the Split CIFAR-10/100 and Split MNIST benchmarks in the
task agnostic inference. We empirically show that we can achieve a better
performance than several state-of-the-art methods for continual learning by
building on the top of selected representation learned by SAM. We also show the
role of the meta-attention mechanism in boosting informative features
corresponding to the input data 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.
