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Learning Transferable Architectures for Scalable Image ...
Learning Transferable Architectures for Scalable Image Recognition Barret Zoph Vijay Vasudevan Jonathon Shlens Quoc V. Le Google Brain Google Brain Google Brain Google Brain [ ] 11 Apr 2018. Abstract 1. Introduction Developing neural network Image classification models often requires significant architecture engineering. Starting Developing neural network Image classification models from the seminal work of [32] on using convolutional archi- often requires significant architecture engineering. In this tectures [17, 34] for ImageNet [11] classification, succes- paper, we study a method to learn the model Architectures sive advancements through architecture engineering have directly on the dataset of interest. As this approach is ex- achieved impressive results [53, 59, 20, 60, 58, 68]. pensive when the dataset is large, we propose to search for In this paper, we study a new paradigm of designing con- an architectural building block on a small dataset and then volutional Architectures and describe a Scalable method to transfer the block to a larger dataset.
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 accu-racy than the best human-invented architectures while hav-ing 9 billion fewer FLOPS – a reduction of 28% in compu-tational demand from the previous state-of-the-art model. When evaluated at different levels of computational cost,
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