  One of the most critical problems in weight-sharing neural architecture
search is the evaluation of candidate models within a predefined search space.
In practice, a one-shot supernet is trained to serve as an evaluator. A
faithful ranking certainly leads to more accurate searching results. However,
current methods are prone to making misjudgments. In this paper, we prove that
their biased evaluation is due to inherent unfairness in the supernet training.
In view of this, we propose two levels of constraints: expectation fairness and
strict fairness. Particularly, strict fairness ensures equal optimization
opportunities for all choice blocks throughout the training, which neither
overestimates nor underestimates their capacity. We demonstrate that this is
crucial for improving the confidence of models' ranking. Incorporating the
one-shot supernet trained under the proposed fairness constraints with a
multi-objective evolutionary search algorithm, we obtain various
state-of-the-art models, e.g., FairNAS-A attains 77.5% top-1 validation
accuracy on ImageNet. The models and their evaluation codes are made publicly
available online http://github.com/fairnas/FairNAS .
