  Differentiable Neural Architecture Search is one of the most popular Neural
Architecture Search (NAS) methods for its search efficiency and simplicity,
accomplished by jointly optimizing the model weight and architecture parameters
in a weight-sharing supernet via gradient-based algorithms. At the end of the
search phase, the operations with the largest architecture parameters will be
selected to form the final architecture, with the implicit assumption that the
values of architecture parameters reflect the operation strength. While much
has been discussed about the supernet's optimization, the architecture
selection process has received little attention. We provide empirical and
theoretical analysis to show that the magnitude of architecture parameters does
not necessarily indicate how much the operation contributes to the supernet's
performance. We propose an alternative perturbation-based architecture
selection that directly measures each operation's influence on the supernet. We
re-evaluate several differentiable NAS methods with the proposed architecture
selection and find that it is able to extract significantly improved
architectures from the underlying supernets consistently. Furthermore, we find
that several failure modes of DARTS can be greatly alleviated with the proposed
selection method, indicating that much of the poor generalization observed in
DARTS can be attributed to the failure of magnitude-based architecture
selection rather than entirely the optimization of its supernet.
