Advances in few-shot learning: reproducing results in PyTorch

Few-shot learning is an exciting field of machine learning which aims to close the gap between machine and human in the challenging task of learning from few examples. In my previous post I provided a high level summary of three cutting edge papers in few-shot learning -- I assume you've either read that, are already familiar with these papers or are in the process of reproducing them yourself. In this post I will guide you through my experience in reproducing the results of these papers on the Omniglot and miniImageNet datasets, including some of the pitfalls and stumbling blocks on the way. Each paper has its own section in which I provide a Github gist with PyTorch code to perform a single parameter update on the model described by the paper. To train the model just have to put that function inside a loop over the training data.