When MAML Can Adapt Fast and How to Assist When It Cannot

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Co-Transport for Class-Incremental Learning (2021, background): “Like [3], a good initialization helps learn a better embedding, which maintains the former knowledge and resists catastrophic forgetting.”
Offline Meta-Reinforcement Learning with Advantage Weighting (2020, background): “This architecture shares the motivation of enabling more expressive adaptation without increasing model expressiveness described in (Arnold et al., 2021) and is similar to latent embedding optimization (Rusu et al., 2019).”