Reviews: Learning to Learn By Self-Critique

Summary: This paper considers few-shot classification and seeks to make use of the unlabeled query data during few-shot classification by training on it with a meta-learned critic loss. The algorithm builds on top of MAML, and has two stages. In the first stage, the model is adapted via gradient descent on the labeled support set. In the second stage, the model is further adapted via a meta-learned critic loss that is a function of a featurization of the model parameters and the unlabeled query set. Originality: The proposed approach strikes me as quite similar to One-Shot Imitation Learning by Domain-Adaptive Meta-Learning (Yu et al. 2018).