Papers › DARTS: Differentiable Architecture Search

DARTS: Differentiable Architecture Search

24 Jun 2018ICLR 2019 5arXiv:1806.09055archive 2025-07-28

Hanxiao Liu, Karen Simonyan, Yiming Yang

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.

Code

59 repositories listed; official and paper-mentioned ones first.

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Tasks

Results from the paper archive 2025-07-28

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Methods

Introduced by this paper: DARTS, Differentiable NAS

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