  Learning in non-stationary environments is one of the biggest challenges in
machine learning. Non-stationarity can be caused by either task drift, i.e.,
the drift in the conditional distribution of labels given the input data, or
the domain drift, i.e., the drift in the marginal distribution of the input
data. This paper aims to tackle this challenge in the context of continuous
domain adaptation, where the model is required to learn new tasks adapted to
new domains in a non-stationary environment while maintaining previously
learned knowledge. To deal with both drifts, we propose variational
domain-agnostic feature replay, an approach that is composed of three
components: an inference module that filters the input data into
domain-agnostic representations, a generative module that facilitates knowledge
transfer, and a solver module that applies the filtered and transferable
knowledge to solve the queries. We address the two fundamental scenarios in
continuous domain adaptation, demonstrating the effectiveness of our proposed
approach for practical usage.
