  The recent progress in neural architecture search (NAS) has allowed scaling
the automated design of neural architectures to real-world domains, such as
object detection and semantic segmentation. However, one prerequisite for the
application of NAS are large amounts of labeled data and compute resources.
This renders its application challenging in few-shot learning scenarios, where
many related tasks need to be learned, each with limited amounts of data and
compute time. Thus, few-shot learning is typically done with a fixed neural
architecture. To improve upon this, we propose MetaNAS, the first method which
fully integrates NAS with gradient-based meta-learning. MetaNAS optimizes a
meta-architecture along with the meta-weights during meta-training. During
meta-testing, architectures can be adapted to a novel task with a few steps of
the task optimizer, that is: task adaptation becomes computationally cheap and
requires only little data per task. Moreover, MetaNAS is agnostic in that it
can be used with arbitrary model-agnostic meta-learning algorithms and
arbitrary gradient-based NAS methods. %We present encouraging results for
MetaNAS with a combination of DARTS and REPTILE on few-shot classification
benchmarks. Empirical results on standard few-shot classification benchmarks
show that MetaNAS with a combination of DARTS and REPTILE yields
state-of-the-art results.
