DARTS: Differentiable Architecture Search
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. Our implementation has been made publicly available to facilitate further research on efficient architecture search algorithms.
- Publication:
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arXiv e-prints
- Pub Date:
- June 2018
- DOI:
- 10.48550/arXiv.1806.09055
- arXiv:
- arXiv:1806.09055
- Bibcode:
- 2018arXiv180609055L
- Keywords:
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- Computer Science - Machine Learning;
- Computer Science - Computation and Language;
- Computer Science - Computer Vision and Pattern Recognition;
- Statistics - Machine Learning
- E-Print:
- Published at ICLR 2019