  Neural Architecture Search (NAS) often trains and evaluates a large number of
architectures. Recent predictor-based NAS approaches attempt to alleviate such
heavy computation costs with two key steps: sampling some
architecture-performance pairs and fitting a proxy accuracy predictor. Given
limited samples, these predictors, however, are far from accurate to locate top
architectures due to the difficulty of fitting the huge search space. This
paper reflects on a simple yet crucial question: if our final goal is to find
the best architecture, do we really need to model the whole space well?. We
propose a paradigm shift from fitting the whole architecture space using one
strong predictor, to progressively fitting a search path towards the
high-performance sub-space through a set of weaker predictors. As a key
property of the weak predictors, their probabilities of sampling better
architectures keep increasing. Hence we only sample a few well-performed
architectures guided by the previously learned predictor and estimate a new
better weak predictor. This embarrassingly easy framework, dubbed WeakNAS,
produces coarse-to-fine iteration to gradually refine the ranking of sampling
space. Extensive experiments demonstrate that WeakNAS costs fewer samples to
find top-performance architectures on NAS-Bench-101 and NAS-Bench-201. Compared
to state-of-the-art (SOTA) predictor-based NAS methods, WeakNAS outperforms all
with notable margins, e.g., requiring at least 7.5x less samples to find global
optimal on NAS-Bench-101. WeakNAS can also absorb their ideas to boost
performance more. Further, WeakNAS strikes the new SOTA result of 81.3% in the
ImageNet MobileNet Search Space. The code is available at
https://github.com/VITA-Group/WeakNAS.
