Reviews: Few-Shot Learning Through an Information Retrieval Lens

This work is a great extension of few shot learning paradigms in deep metric learning. Rather than considering pairs of samples for metric learning (siamese networks), or 3 examples (anchor, positive, and negative -- triplet learning), the authors propose to use the full minibatchs' relationships or ranking with respect to the anchor sample. This makes sense from the structured prediction, and efficient learning. Incorporating mean average precision into the loss directly, allows for optimization of the actual task at hand. Some comments: 1. t would be great to see more experimental results, on other datasets (face recognition). Not clear how to set \lambda, as in a number of cases, a wrong value for \lambda leads to weak results.