  We develop an algorithm to improve the performance of a pre-trained model
under concept shift without retraining the model from scratch when only
unannotated samples of initial concepts are accessible. We model this problem
as a domain adaptation problem, where the source domain data is inaccessible
during model adaptation. The core idea is based on consolidating the
intermediate internal distribution, learned to represent the source domain
data, after adapting the model. We provide theoretical analysis and conduct
extensive experiments to demonstrate that the proposed method is effective.
