Reinforced Evolutionary Neural Architecture Search
release_nnjs54q42neqjpp6hkz3mzcsaa
by
Yukang Chen, Gaofeng Meng, Qian Zhang, Shiming Xiang, Chang Huang,
Lisen Mu, Xinggang Wang
2018
Abstract
Neural Architecture Search (NAS) is an important yet challenging task in
network design due to its high computational consumption. To address this
issue, we propose the Reinforced Evolutionary Neural Architecture Search (RE-
NAS), which is an evolutionary method with the reinforced mutation for NAS. Our
method integrates reinforced mutation into an evolution algorithm for neural
architecture exploration, in which a mutation controller is introduced to learn
the effects of slight modifications and make mutation actions. The reinforced
mutation controller guides the model population to evolve efficiently.
Furthermore, as child models can inherit parameters from their parents during
evolution, our method requires very limited computational resources. In
experiments, we conduct the proposed search method on CIFAR-10 and obtain a
powerful network architecture, RENASNet. This architecture achieves a
competitive result on CIFAR-10. The explored network architecture is
transferable to ImageNet and achieves a new state-of-the-art accuracy, i.e.,
75.7% top-1 accuracy with 5.36M parameters on mobile ImageNet. We further test
its performance on semantic segmentation with DeepLabv3 on the PASCAL VOC.
RENASNet outperforms MobileNet-v1, MobileNet-v2 and NASNet. It achieves 75.83%
mIOU without being pre-trained on COCO.
In text/plain
format
Archived Files and Locations
application/pdf 1.6 MB
file_xc3qxqkqg5g3vaa2dv3qr3nwoy
|
arxiv.org (repository) web.archive.org (webarchive) |
1808.00193v2
access all versions, variants, and formats of this works (eg, pre-prints)