NAS-Bench-1Shot1: Benchmarking and Dissecting One-shot Neural Architecture Search

Arber Zela, Julien Siems, Frank Hutter

Abstract:

One-shot neural architecture search (NAS) has played a crucial role in making
NAS methods computationally feasible in practice. Nevertheless, there is still a
lack of understanding on how these weight-sharing algorithms exactly work due
to the many factors controlling the dynamics of the process. In order to allow
a scientific study of these components, we introduce a general framework for
one-shot NAS that can be instantiated to many recently-introduced variants and
introduce a general benchmarking framework that draws on the recent large-scale
tabular benchmark NAS-Bench-101 for cheap anytime evaluations of one-shot
NAS methods. To showcase the framework, we compare several state-of-the-art
one-shot NAS methods, examine how sensitive they are to their hyperparameters
and how they can be improved by tuning their hyperparameters, and compare their
performance to that of blackbox optimizers for NAS-Bench-101.

Code: https://github.com/automl/nasbench-1shot1

Keywords: Neural Architecture Search, Deep Learning, Computer Vision

Community Implementations: [ 1 code implementation](https://www.catalyzex.com/paper/arxiv:2001.10422/code)