  Model-agnostic meta-learning (MAML) is one of the most popular and widely
adopted meta-learning algorithms, achieving remarkable success in various
learning problems. Yet, with the unique design of nested inner-loop and
outer-loop updates, which govern the task-specific and meta-model-centric
learning, respectively, the underlying learning objective of MAML remains
implicit and thus impedes a more straightforward understanding of it. In this
paper, we provide a new perspective of the working mechanism of MAML. We
discover that MAML is analogous to a meta-learner using a supervised
contrastive objective. The query features are pulled towards the support
features of the same class and against those of different classes. Such
contrastiveness is experimentally verified via an analysis based on the cosine
similarity. Moreover, we reveal that vanilla MAML has an undesirable
interference term originating from the random initialization and the cross-task
interaction. We thus propose a simple but effective technique, zeroing trick,
to alleviate the interference. Extensive experiments are conducted on both
mini-ImageNet and Omniglot datasets to demonstrate the consistent improvement
brought by our proposed method, validating its effectiveness.
