Overcoming Concept Shift in Domain-Aware Settings through Consolidated Internal Distributions

We develop an algorithm to improve the predictive 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 on five benchmark datasets to demonstrate that the proposed method is effective.