Abstract:Lifelong domain adaptation remains a challenging task in machine learning due to the differences among the domains and the unavailability of historical data. The ultimate goal is to learn the distributional shifts while retaining the previously gained knowledge. Inspired by the Complementary Learning Systems (CLS) theory [31], we propose a novel framework called Lifelong Self-Supervised Domain Adaptation (LLEDA). LLEDA addresses catastrophic forgetting by replaying hidden representations rather than raw data p… Show more
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