Reviews: Learning to Propagate for Graph Meta-Learning

The prototype embeddings are refined iteratively through the current presentation and neighboring prototypes from similar tasks using a gating mechanism. The refinement process is similar to the multi-head mechanism but is new and unique. The evaluation process on ImageNet is reasonable which demonstrates the effectiveness of the method on both closely-related and distant tasks. However, as the data are created by the authors and not released, it may be hard to reproduce the results based on the current information in the submission. In Line 173, the authors mention it is possible to use the history prototypes for better training performance for GPN model.