  Training-free metrics (a.k.a. zero-cost proxies) are widely used to avoid
resource-intensive neural network training, especially in Neural Architecture
Search (NAS). Recent studies show that existing training-free metrics have
several limitations, such as limited correlation and poor generalisation across
different search spaces and tasks. Hence, we propose Sample-Wise Activation
Patterns and its derivative, SWAP-Score, a novel high-performance training-free
metric. It measures the expressivity of networks over a batch of input samples.
The SWAP-Score is strongly correlated with ground-truth performance across
various search spaces and tasks, outperforming 15 existing training-free
metrics on NAS-Bench-101/201/301 and TransNAS-Bench-101. The SWAP-Score can be
further enhanced by regularisation, which leads to even higher correlations in
cell-based search space and enables model size control during the search. For
example, Spearman's rank correlation coefficient between regularised SWAP-Score
and CIFAR-100 validation accuracies on NAS-Bench-201 networks is 0.90,
significantly higher than 0.80 from the second-best metric, NWOT. When
integrated with an evolutionary algorithm for NAS, our SWAP-NAS achieves
competitive performance on CIFAR-10 and ImageNet in approximately 6 minutes and
9 minutes of GPU time respectively.
