  Neural architecture search (NAS) is a recent methodology for automating the
design of neural network architectures. Differentiable neural architecture
search (DARTS) is a promising NAS approach that dramatically increases search
efficiency. However, it has been shown to suffer from performance collapse,
where the search often leads to detrimental architectures. Many recent works
try to address this issue of DARTS by identifying indicators for early
stopping, regularising the search objective to reduce the dominance of some
operations, or changing the parameterisation of the search problem. In this
work, we hypothesise that performance collapses can arise from poor local
optima around typical initial architectures and weights. We address this issue
by developing a more global optimisation scheme that is able to better explore
the space without changing the DARTS problem formulation. Our experiments show
that our changes in the search algorithm allow the discovery of architectures
with both better test performance and fewer parameters.
