Weaknesses: - Problem setting: The motivation behind the combination of domain adaptation and online learning can be further elaborated. Online learning is mainly used when it is computationally infeasible to train over the entire dataset. In domain adaptation, only very few examples are used for the adaptation process. Therefore it is usually feasible to simply store all the data and train in an off-line manner. As shown in Table 1, the performance of the proposed framework is weak, making it impractical to choose the online learning setting.

Domain adaptation aims at knowledge transfer from a labeled source domain to an unlabeled target domain. Current domain adaptation methods have made substantial advances in adapting discrete domains. However, this can be unrealistic in real-world applications, where target data usually comes in an online and continually evolving manner as small batches, posing challenges to classic domain adaptation paradigm: (1) Mainstream domain adaptation methods are tailored to stationary target domains, and can fail in non-stationary environments. To tackle these challenges, we propose a meta-adaptation framework which enables the learner to adapt to continually evolving target domain without catastrophic forgetting. Our framework comprises of two components: a meta-objective of learning representations to adapt to evolving domains, enabling meta-learning for unsupervised domain adaptation; and a meta-adapter for learning to adapt without forgetting, reserving knowledge from previous target data.