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Most existing neural architecture search (NAS) benchmarks and algorithmsprioritize well-studied tasks, e.g. image classification on CIFAR or ImageNet.This makes the performance of NAS approaches in more diverse areas poorlyunderstood. In this paper, we present NAS-Bench-360, a benchmark suite toevaluate methods on domains beyond those traditionally studied in architecturesearch, and use it to address the following question: do state-of-the-art NASmethods perform well on diverse tasks? To construct the benchmark, we curateten tasks spanning a diverse array of application domains, dataset sizes,problem dimensionalities, and learning objectives. Each task is carefullychosen to interoperate with modern CNN-based search methods while possiblybeing far-afield from its original development domain. To speed up and reducethe cost of NAS research, for two of the tasks we release the precomputedperformance of 15,625 architectures comprising a standard CNN search space.Experimentally, we show the need for more robust NAS evaluation of the kindNAS-Bench-360 enables by showing that several modern NAS procedures performinconsistently across the ten tasks, with many catastrophically poor results.We also demonstrate how NAS-Bench-360 and its associated precomputed resultswill enable future scientific discoveries by testing whether several recenthypotheses promoted in the NAS literature hold on diverse tasks. NAS-Bench-360is hosted at https://nb360.ml.cmu.edu.

NAS-Bench-360: Benchmarking Neural Architecture Search on Diverse Tasks