  Recognizing new objects by learning from a few labeled examples in an
evolving environment is crucial to obtain excellent generalization ability for
real-world machine learning systems. A typical setting across current meta
learning algorithms assumes a stationary task distribution during meta
training. In this paper, we explore a more practical and challenging setting
where task distribution changes over time with domain shift. Particularly, we
consider realistic scenarios where task distribution is highly imbalanced with
domain labels unavailable in nature. We propose a kernel-based method for
domain change detection and a difficulty-aware memory management mechanism that
jointly considers the imbalanced domain size and domain importance to learn
across domains continuously. Furthermore, we introduce an efficient adaptive
task sampling method during meta training, which significantly reduces task
gradient variance with theoretical guarantees. Finally, we propose a
challenging benchmark with imbalanced domain sequences and varied domain
difficulty. We have performed extensive evaluations on the proposed benchmark,
demonstrating the effectiveness of our method. We made our code publicly
available.
