DARTS: Differentiable Architecture Search
Jul 17, 2018
DARTS: Differentiable Architecture Search
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Liu, Hanxiao;
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Simonyan, Karen;
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Yang, Yiming;
Abstract: This paper addresses the scalability challenge of architecture search by formulating the task in a differentiable manner. Unlike conventional approaches of applying evolution or reinforcement learning over a discrete and non-differentiable search space, our method is based on the continuous relaxation of the architecture representation, allowing efficient search of the architecture using gradient descent. Extensive experiments on CIFAR-10, ImageNet, Penn Treebank and WikiText-2 show that our algorithm excels in discovering high-performance convolutional architectures for image classification and recurrent architectures for language modeling, while being orders of magnitude faster than state-of-the-art non-differentiable techniques.
https://arxiv.org/abs/1806.09055v1
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