  We address the challenge of training a large supernet for the object
detection task, using a relatively small amount of training data. Specifically,
we propose an efficient supernet-based neural architecture search (NAS) method
that uses search space pruning. The search space defined by the supernet is
pruned by removing candidate models that are predicted to perform poorly. To
effectively remove the candidates over a wide range of resource constraints, we
particularly design a performance predictor for supernet, called path filter,
which is conditioned by resource constraints and can accurately predict the
relative performance of the models that satisfy similar resource constraints.
Hence, supernet training is more focused on the best-performing candidates. Our
path filter handles prediction for paths with different resource budgets.
Compared to once-for-all, our proposed method reduces the computational cost of
the optimal network architecture by 30% and 63%, while yielding better
accuracy-floating point operations Pareto front (0.85 and 0.45 points of
improvement on average precision for Pascal VOC and COCO, respectively).
