  Differentiable Architecture Search (DARTS) has attracted a lot of attention
due to its simplicity and small search costs achieved by a continuous
relaxation and an approximation of the resulting bi-level optimization problem.
However, DARTS does not work robustly for new problems: we identify a wide
range of search spaces for which DARTS yields degenerate architectures with
very poor test performance. We study this failure mode and show that, while
DARTS successfully minimizes validation loss, the found solutions generalize
poorly when they coincide with high validation loss curvature in the
architecture space. We show that by adding one of various types of
regularization we can robustify DARTS to find solutions with less curvature and
better generalization properties. Based on these observations, we propose
several simple variations of DARTS that perform substantially more robustly in
practice. Our observations are robust across five search spaces on three image
classification tasks and also hold for the very different domains of disparity
estimation (a dense regression task) and language modelling.
