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https://www.arxiv-vanity.com/papers/2102.11535/
X. Dong and Y. Yang (2019) Searching for a robust neural architecture in four gpu hours. In Proceedings of the IEEE Conference on computer vision and pattern recognition, pp. 1761–1770. Cited by: §1, §2, Table 1, Table 2, Table 3. X. Dong and Y. Yang (2020) Nas-bench-102: extending the scope of reproducible neural architecture search.
https://arxiv.org/abs/2102.11535v3
Further on, we design a pruning-based NAS mechanism to achieve a more flexible and superior trade-off between the trainability and expressivity during the search. In NAS-Bench-201 and DARTS search spaces, TE-NAS completes high-quality search but only costs 0.5 and 4 GPU hours with one 1080Ti on CIFAR-10 and ImageNet, respectively.
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https://www.arxiv-vanity.com/papers/1709.05011/
FireCaffe [Iandola et al.2015] [Iandola et al.2016] demonstrated scaling the training of GoogleNet to 128 Nvidia K20 GPUs with a batch size of 1K for 72 epochs and a total training time of 10.5 hours. Although large batch size can lead to a significant loss in accuracy, using a warm-up scheme coupled with a linear scaling rule, researchers at Facebook [Goyal et al.2017] were able to scale the ...
https://openreview.net/pdf?id=Cnon5ezMHtu
only costs 0.5 GPU hours to search on CIFAR10, and 4 GPU hours on ImageNet, respectively, setting the new record for ultra-efficient yet high-quality NAS. 2 RELATED WORKS Neural architecture search (NAS) is recently proposed to accelerate the principled and automated discovery of high-performance networks.
https://www.imanet.org/?null
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