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Continual learning studies agents that learn from streams of tasks withoutforgetting previous ones while adapting to new ones. Two recentcontinual-learning scenarios have opened new avenues of research. Inmeta-continual learning, the model is pre-trained to minimize catastrophicforgetting of previous tasks. In continual-meta learning, the aim is to trainagents for faster remembering of previous tasks through adaptation. In theiroriginal formulations, both methods have limitations. We stand on theirshoulders to propose a more general scenario, OSAKA, where an agent mustquickly solve new (out-of-distribution) tasks, while also requiring fastremembering. We show that current continual learning, meta-learning,meta-continual learning, and continual-meta learning techniques fail in thisnew scenario. We propose Continual-MAML, an online extension of the popularMAML algorithm as a strong baseline for this scenario. We empirically show thatContinual-MAML is better suited to the new scenario than the aforementionedmethodologies, as well as standard continual learning and meta-learningapproaches.

Online Fast Adaptation and Knowledge Accumulation: a New Approach to Continual Learning