  Improving the efficiency of Neural Architecture Search (NAS) is a challenging
but significant task that has received much attention. Previous works mainly
adopted the Differentiable Architecture Search (DARTS) and improved its search
strategies or modules to enhance search efficiency. Recently, some methods have
started considering data reduction for speedup, but they are not tightly
coupled with the architecture search process, resulting in sub-optimal
performance. To this end, this work pioneers an exploration into the critical
role of dataset characteristics for DARTS bi-level optimization, and then
proposes a novel Bi-level Data Pruning (BDP) paradigm that targets the weights
and architecture levels of DARTS to enhance efficiency from a data perspective.
Specifically, we introduce a new progressive data pruning strategy that
utilizes supernet prediction dynamics as the metric, to gradually prune
unsuitable samples for DARTS during the search. An effective automatic class
balance constraint is also integrated into BDP, to suppress potential class
imbalances resulting from data-efficient algorithms. Comprehensive evaluations
on the NAS-Bench-201 search space, DARTS search space, and MobileNet-like
search space validate that BDP reduces search costs by over 50% while achieving
superior performance when applied to baseline DARTS. Besides, we demonstrate
that BDP can harmoniously integrate with advanced DARTS variants, like PC-DARTS
and \b{eta}-DARTS, offering an approximately 2 times speedup with minimal
performance compromises.
