NAS-Bench-201: Extending the Scope of Reproducible Neural Architecture
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- URL: http://arxiv.org/abs/2001.00326v2
- Date: Wed, 15 Jan 2020 12:38:55 GMT
- Title: NAS-Bench-201: Extending the Scope of Reproducible Neural Architecture
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- Authors: Xuanyi Dong and Yi Yang
- Abstract summary: We propose an extension to NAS-Bench-101: NAS-Bench-201 with a different search space, results on multiple datasets, and more diagnostic information.
NAS-Bench-201 has a fixed search space and provides a unified benchmark for almost any up-to-date NAS algorithms.
We provide additional diagnostic information such as fine-grained loss and accuracy, which can give inspirations to new designs of NAS algorithms.
- Score: 55.12928953187342
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Neural architecture search (NAS) has achieved breakthrough success in a great
number of applications in the past few years. It could be time to take a step
back and analyze the good and bad aspects in the field of NAS. A variety of
algorithms search architectures under different search space. These searched
architectures are trained using different setups, e.g., hyper-parameters, data
augmentation, regularization. This raises a comparability problem when
comparing the performance of various NAS algorithms. NAS-Bench-101 has shown
success to alleviate this problem. In this work, we propose an extension to
NAS-Bench-101: NAS-Bench-201 with a different search space, results on multiple
datasets, and more diagnostic information. NAS-Bench-201 has a fixed search
space and provides a unified benchmark for almost any up-to-date NAS
algorithms. The design of our search space is inspired from the one used in the
most popular cell-based searching algorithms, where a cell is represented as a
DAG. Each edge here is associated with an operation selected from a predefined
operation set. For it to be applicable for all NAS algorithms, the search space
defined in NAS-Bench-201 includes all possible architectures generated by 4
nodes and 5 associated operation options, which results in 15,625 candidates in
total. The training log and the performance for each architecture candidate are
provided for three datasets. This allows researchers to avoid unnecessary
repetitive training for selected candidate and focus solely on the search
algorithm itself. The training time saved for every candidate also largely
improves the efficiency of many methods. We provide additional diagnostic
information such as fine-grained loss and accuracy, which can give inspirations
to new designs of NAS algorithms. In further support, we have analyzed it from
many aspects and benchmarked 10 recent NAS algorithms.
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