  Most existing neural architecture search (NAS) algorithms are dedicated to
and evaluated by the downstream tasks, e.g., image classification in computer
vision. However, extensive experiments have shown that, prominent neural
architectures, such as ResNet in computer vision and LSTM in natural language
processing, are generally good at extracting patterns from the input data and
perform well on different downstream tasks. In this paper, we attempt to answer
two fundamental questions related to NAS. (1) Is it necessary to use the
performance of specific downstream tasks to evaluate and search for good neural
architectures? (2) Can we perform NAS effectively and efficiently while being
agnostic to the downstream tasks? To answer these questions, we propose a novel
and generic NAS framework, termed Generic NAS (GenNAS). GenNAS does not use
task-specific labels but instead adopts regression on a set of manually
designed synthetic signal bases for architecture evaluation. Such a
self-supervised regression task can effectively evaluate the intrinsic power of
an architecture to capture and transform the input signal patterns, and allow
more sufficient usage of training samples. Extensive experiments across 13 CNN
search spaces and one NLP space demonstrate the remarkable efficiency of GenNAS
using regression, in terms of both evaluating the neural architectures
(quantified by the ranking correlation Spearman's rho between the approximated
performances and the downstream task performances) and the convergence speed
for training (within a few seconds).
