  Few-Shot Learning (FSL) is a topic of rapidly growing interest. Typically, in
FSL a model is trained on a dataset consisting of many small tasks (meta-tasks)
and learns to adapt to novel tasks that it will encounter during test time.
This is also referred to as meta-learning. Another topic closely related to
meta-learning with a lot of interest in the community is Neural Architecture
Search (NAS), automatically finding optimal architecture instead of engineering
it manually. In this work, we combine these two aspects of meta-learning. So
far, meta-learning FSL methods have focused on optimizing parameters of
pre-defined network architectures, in order to make them easily adaptable to
novel tasks. Moreover, it was observed that, in general, larger architectures
perform better than smaller ones up to a certain saturation point (where they
start to degrade due to over-fitting). However, little attention has been given
to explicitly optimizing the architectures for FSL, nor to an adaptation of the
architecture at test time to particular novel tasks. In this work, we propose
to employ tools inspired by the Differentiable Neural Architecture Search
(D-NAS) literature in order to optimize the architecture for FSL without
over-fitting. Additionally, to make the architecture task adaptive, we propose
the concept of `MetAdapt Controller' modules. These modules are added to the
model and are meta-trained to predict the optimal network connections for a
given novel task. Using the proposed approach we observe state-of-the-art
results on two popular few-shot benchmarks: miniImageNet and FC100.
