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文件名称：Learning Transferable Architectures for Scalable Image Recognition
文件大小：5.99MB
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更新时间：2021-10-08 04:00:20
图像处理 CVPR 2018
Developing neural network image classification models
often requires significant architecture engineering. In this
paper, we study a method to learn the model architectures
directly on the dataset of interest. As this approach is expensive
when the dataset is large, we propose to search for
an architectural building block on a small dataset and then
transfer the block to a larger dataset. The key contribution
of this work is the design of a new search space (which
we call the “NASNet search space”) which enables transferability.
In our experiments, we search for the best convolutional
layer (or “cell”) on the CIFAR-10 dataset and
then apply this cell to the ImageNet dataset by stacking together
more copies of this cell, each with their own parameters
to design a convolutional architecture, which we name
a “NASNet architecture”. We also introduce a new regularization
technique called ScheduledDropPath that significantly
improves generalization in the NASNet models. On
CIFAR-10 itself, a NASNet found by our method achieves
2.4% error rate, which is state-of-the-art. Although the cell
is not searched for directly on ImageNet, a NASNet constructed
from the best cell achieves, among the published
works, state-of-the-art accuracy of 82.7% top-1 and 96.2%
top-5 on ImageNet. Our model is 1.2% better in top-1 accuracy
than the best human-invented architectures while having
9 billion fewer FLOPS – a reduction of 28% in computational
demand from the previous state-of-the-art model.
When evaluated at different levels of computational cost,
accuracies of NASNets exceed those of the state-of-the-art
human-designed models. For instance, a small version of
NASNet also achieves 74% top-1 accuracy, which is 3.1%
better than equivalently-sized, state-of-the-art models for
mobile platforms. Finally, the image features learned from
image classification are generically useful and can be transferred
to other computer vision problems. On the task of object
detection, the learned features by NASNet used with the
Faster-RCNN framework surpass state-of-the-art by 4.0%
achieving 43.1% mAP on the COCO dataset