  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 https://github.com/automl/nas-bench-x11.
