Few-shot learning using MAML and Protonets

What happens when you have a huge number of classes and a few data points for each class (like omniglot )? You need few-shot/meta-learning where you learn initializations of weights using tasks. This is done for N classes having k examples each. The model must do well with a small k for any combination of N classes (potentially new) (and a support set).

In this project I implemented MAML and Prototype Learning (2 popularly used few-shot learning algorithms).

Note: You can find the implementation here.