Continuous Unsupervised Domain Adaptation Using Stabilized Representations and Experience Replay

We introduce an algorithm for tackling the problem of unsupervised domain adaptation (UDA) in continual learning (CL) scenarios. The primary objective is to maintain model generalization under domain shift when new domains arrive continually through updating a base model when only unlabeled data is accessible in subsequent tasks. While there are many existing UDA algorithms, they typically require access to both the source and target domain datasets simultaneously. Conversely, existing CL approaches can handle tasks that all have labeled data. …

algorithm continual continuous cs.cv cs.lg data domain domain adaptation domains experience model generalization shift tasks through unsupervised