Neural architecture search: Difference between revisions

Revision as of 22:28, 2 November 2022

Revision as of 10:45, 26 November 2022

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==Reinforcement learning==

[[Reinforcement learning]] (RL) can underpin a NAS search strategy. Barret Zoph etand [[Quoc Viet al.Le]]<ref name="Zoph 2016" /> applied NAS with RL targeting the [[CIFAR-10]] dataset and achieved a network architecture that rivals the best manually-designed architecture for accuracy, with an error rate of 3.65, 0.09 percent better and 1.05x faster than a related hand-designed model. On the [[Treebank|Penn Treebank]] dataset, that model composed a recurrent cell that outperforms [[Long short-term memory|LSTM]], reaching a test set perplexity of 62.4, or 3.6 perplexity better than the prior leading system. On the PTB character language modeling task it achieved bits per character of 1.214.<ref name="Zoph 2016">{{cite arXiv|last1=Zoph|first1=Barret|last2=Le|first2=Quoc V.|date=2016-11-04|title=Neural Architecture Search with Reinforcement Learning|eprint=1611.01578 |class=cs.LG}}</ref>

Learning a model architecture directly on a large dataset can be a lengthy process. NASNet<ref name="Zoph 2017" /><ref>{{Cite news|url=https://research.googleblog.com/2017/11/automl-for-large-scale-image.html|title=AutoML for large scale image classification and object detection|last1=Zoph|first1=Barret|date=November 2, 2017|work=Research Blog|access-date=2018-02-20|last2=Vasudevan|first2=Vijay|language=en-US|last3=Shlens|first3=Jonathon|last4=Le|first4=Quoc V.}}</ref> addressed this issue by transferring a building block designed for a small dataset to a larger dataset. The design was constrained to use two types of [[Convolutional neural network|convolutional]] cells to return feature maps that serve two main functions when convoluting an input feature map: ''normal cells'' that return maps of the same extent (height and width) and ''reduction cells'' in which the returned feature map height and width is reduced by a factor of two. For the reduction cell, the initial operation applied to the cell’s inputs uses a stride of two (to reduce the height and width).<ref name="Zoph 2017" /> The learned aspect of the design included elements such as which lower layer(s) each higher layer took as input, the transformations applied at that layer and to merge multiple outputs at each layer. In the studied example, the best convolutional layer (or "cell") was designed for the CIFAR-10 dataset and then applied to the [[ImageNet]] dataset by stacking copies of this cell, each with its own parameters. The approach yielded accuracy of 82.7% top-1 and 96.2% top-5. This exceeded the best human-invented architectures at a cost of 9 billion fewer [[FLOPS]]—a reduction of 28%. The system continued to exceed the manually-designed alternative at varying computation levels. The image features learned from image classification can be transferred to other computer vision problems. E.g., for object detection, the learned cells integrated with the Faster-RCNN framework improved performance by 4.0% on the [[COCO (dataset)|COCO]] dataset.<ref name="Zoph 2017" />