While early research in neural architecture search (NAS) required extreme
computational resources, the recent releases of tabular and surrogate
benchmarks have greatly increased the speed and reproducibility of NAS
research. However, two of the most popular benchmarks do not provide the full
training information for each architecture. As a result, on these benchmarks it
is not possible to run many types of multi-fidelity techniques, such as
learning curve extrapolation, that require evaluating architectures at
arbitrary epochs. In this work, we present a method using singular value
decomposition and noise modeling to create surrogate benchmarks, NAS-Bench-111,
NAS-Bench-311, and NAS-Bench-NLP11, that output the full training information
for each architecture, rather than just the final validation accuracy. We
demonstrate the power of using the full training information by introducing a
learning curve extrapolation framework to modify single-fidelity algorithms,
showing that it leads to improvements over popular single-fidelity algorithms
which claimed to be state-of-the-art upon release. Our code and pretrained
models are available at this https URL

本论文介绍了一种新的方法，使用奇异值分解和噪声建模来创建代理基准，该方法可以替代缺乏完整训练信息的流行基准，如 NAS-Bench-111、NAS-Bench-311 和 NAS-Bench-NLP11，从而实现对多保真技术的评估。展示了使用全面的训练信息修改单保真算法的学习曲线外推框架的强大能力，并表明这种方法比当时声称是最先进的保真算法有更好的表现。