  In meta-learning approaches, it is difficult for a practitioner to make sense
of what kind of representations the model employs. Without this ability, it can
be difficult to both understand what the model knows as well as to make
meaningful corrections. To address these challenges, we introduce AutoProtoNet,
which builds interpretability into Prototypical Networks by training an
embedding space suitable for reconstructing inputs, while remaining convenient
for few-shot learning. We demonstrate how points in this embedding space can be
visualized and used to understand class representations. We also devise a
prototype refinement method, which allows a human to debug inadequate
classification parameters. We use this debugging technique on a custom
classification task and find that it leads to accuracy improvements on a
validation set consisting of in-the-wild images. We advocate for
interpretability in meta-learning approaches and show that there are
interactive ways for a human to enhance meta-learning algorithms.

Machine Learning