D-DARTS: Distributed Differentiable Architecture Search

• URL: http://arxiv.org/abs/2108.09306v1
• Date: Fri, 20 Aug 2021 09:07:01 GMT
• Title: D-DARTS: Distributed Differentiable Architecture Search
• Authors: Alexandre Heuillet, Hedi Tabia, Hichem Arioui, Kamal Youcef-Toumi
• Abstract summary: Differentiable ARchiTecture Search (DARTS) is one of the most trending Neural Architecture Search (NAS) methods. We propose D-DARTS, a novel solution that addresses this problem by nesting several neural networks at cell-level.
• Score: 75.12821786565318
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Differentiable ARchiTecture Search (DARTS) is one of the most trending Neural Architecture Search (NAS) methods, drastically reducing search cost by resorting to Stochastic Gradient Descent (SGD) and weight-sharing. However, it also greatly reduces the search space, thus excluding potential promising architectures from being discovered. In this paper, we propose D-DARTS, a novel solution that addresses this problem by nesting several neural networks at cell-level instead of using weight-sharing to produce more diversified and specialized architectures. Moreover, we introduce a novel algorithm which can derive deeper architectures from a few trained cells, increasing performance and saving computation time. Our solution is able to provide state-of-the-art results on CIFAR-10, CIFAR-100 and ImageNet while using significantly less parameters than previous baselines, resulting in more hardware-efficient neural networks.

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