  In continual and lifelong learning, good representation learning can help
increase performance and reduce sample complexity when learning new tasks.
There is evidence that representations do not suffer from "catastrophic
forgetting" even in plain continual learning, but little further fact is known
about its characteristics. In this paper, we aim to gain more understanding
about representation learning in continual learning, especially on the feature
forgetting problem. We devise a protocol for evaluating representation in
continual learning, and then use it to present an overview of the basic trends
of continual representation learning, showing its consistent deficiency and
potential issues. To study the feature forgetting problem, we create a
synthetic dataset to identify and visualize the prevalence of feature
forgetting in neural networks. Finally, we propose a simple technique using
gating adapters to mitigate feature forgetting. We conclude by discussing that
improving representation learning benefits both old and new tasks in continual
learning.
