3 years ago

Learning Transferable Architectures for Scalable Image Recognition.

Vijay Vasudevan, Jonathon Shlens, Barret Zoph, Quoc V. Le

Developing image classification models often requires significant architecture engineering. In this paper, we attempt to automate this engineering process by learning the model architectures directly on the dataset of interest. As this approach is expensive when the dataset is large, we propose to learn an architectural building block on a small dataset that can be transferred to a large dataset. In our experiments, we search for the best convolutional layer (or "cell") on the CIFAR-10 dataset and then apply this learned cell to the ImageNet dataset by stacking together more copies of this cell, each with their own parameters. Although the cell is not learned directly on ImageNet, an architecture constructed from the best learned cell achieves, among the published work, 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% from the previous state of the art model. When evaluated at different levels of computational cost, accuracies of networks constructed from the cells exceed those of the state-of-the-art human-designed models. For instance, a smaller network constructed from the best cell 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 used with Faster-RCNN framework surpass state-of-the-art by 4.0% achieving 43.1% mAP on the COCO dataset.

Publisher URL: http://arxiv.org/abs/1707.07012

DOI: arXiv:1707.07012v2

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