DLW-NAS: Differentiable Light-Weight Neural Architecture Search

Han K, Wang Y, Tian Q, Guo J, Xu C, Xu C. Ghostnet: more features from cheap operations. In: CVPR. 2020. p. 1577–586.

Sun Y, Xue B, Zhang M, Yen GG, Lv J. Automatically designing CNN architectures using the genetic algorithm for image classification. IEEE Trans Cybern. 2020;50(9):3840–54.

Article  Google Scholar 

Orsic M, Segvic S. Efficient semantic segmentation with pyramidal fusion. Pattern Recognit. 2021;110:107611.

Yang L, Wang H, Zeng Q, Liu Y, Bian G. A hybrid deep segmentation network for fundus vessels via deep-learning framework. Neurocomputing. 2021;448:168–78.

Article  Google Scholar 

Ebadi N, Jozani M, Choo KKR, Rad P. A memory network information retrieval model for identification of news misinformation. IEEE Transactions on Big Data. 2021.

Mao Y, Zhong G, Wang H, Huang K. MCRN: a new content-based music classification and recommendation network. In: ICONIP, vol. 1332. 2020. p. 771–79.

Zoph B, Le QV. Neural architecture search with reinforcement learning. In: ICLR. 2017.

Liu H, Simonyan K, Yang Y. DARTS: differentiable architecture search. In: ICLR. 2019.

Zhao J, Zhang R, Zhou Z, Chen S, Liu Q. A neural architecture search method based on gradient descent for remaining useful life estimation. Neurocomputing. 2021;438(1).

Mao Y, Zhong G, Wang Y, Deng Z. Differentiable light-weight architecture search. In: IEEE International Conference on Multimedia and Expo (ICME). 2021. p. 1–6. https://doi.org/10.1109/ICME51207.2021.9428132.

Iandola FN, Moskewicz MW, Ashraf K, Han S, Dally WJ, Keutzer K. Squeezenet: Alexnet-level accuracy with 50x fewer parameters and <1mb model size. CoRR abs/1602.07360. 2016.

Howard AG, Zhu M, Chen B, Kalenichenko D, Wang W, Weyand T, Andreetto M, Adam H. Mobilenets: efficient convolutional neural networks for mobile vision applications. CoRR abs/1704.04861. 2017.

Zhang X, Zhou X, Lin M, Sun J. Shufflenet: an extremely efficient convolutional neural network for mobile devices. In: CVPR. 2018. p. 6848–6856.

Cai H, Gan C, Wang T, Zhang Z, Han S. Once-for-All: train one network and specialize it for efficient deployment. In: ICLR. 2020.

Wu B, Dai X, Zhang P, Wang Y, Sun F, Wu Y, Tian Y, Vajda P, Jia Y, Keutzer K. FBNet: hardware-aware efficient ConvNet design via differentiable neural architecture search. In: CVPR. 2019. p. 10734–10742.

Cai H, Zhu L, Han S. ProxylessNAS: direct neural architecture search on target task and hardware. In: ICLR. 2019.

Zhou H, Yang M, Wang J, Pan W. BayesNAS: a Bayesian approach for neural architecture search. In: ICML, vol. 97. 2019. p. 7603–613.

Zhang X, Huang Z, Wang N. You only search once: single shot neural architecture search via direct sparse optimization. CoRR abs/1811.01567. 2019.

Weng Y, Zhou T, Liu L, Xia C. Automatic convolutional neural architecture search for image classification under different scenes. IEEE Access. 2019;7:38495–506.

Article  Google Scholar 

Li X, Wang W, Hu X, Yang J. Selective kernel networks. In: CVPR. 2019. p. 510–19.

Li G, Zhang X, Wang Z, Li Z, Zhang T. STACNAS: towards stable and consistent optimization for differentiable neural architecture search. CoRR abs/1909.11926. 2019.

Wu Y, Liu A, Huang Z, Zhang S, Gool LV. Neural architecture search as sparse supernet. CoRR abs/2007.16112. 2020.

Krizhevsky A. Learning multiple layers of features from tiny images. University of Toronto. 2012.

Deng J, Dong W, Socher R, Li L, Li K, Li F. Imagenet: a large-scale hierarchical image database. In: CVPR. 2009. p. 248–55.

Chen X, Xie L, Wu J, Tian Q. Progressive differentiable architecture search: bridging the depth gap between search and evaluation. In: ICCV. 2019. p. 1294–303

Szegedy C, Liu W, Jia Y, Sermanet P, Reed SE, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A. Going deeper with convolutions. In: CVPR. 2015. p. 1–9.

Devries T, Taylor GW. Improved regularization of convolutional neural networks with cutout. CoRR abs/1708.04552. 2017

Huang G, Liu Z, vander Maaten L, Weinberger KQ. Densely connected convolutional networks. In: CVPR. 2017. p. 2261–269.

Li L, Talwalkar A. Random search and reproducibility for neural architecture search. In: Proceedings of the Thirty-Fifth Conference on Uncertainty in Artificial Intelligence, UAI, Tel Aviv, Israel, July 22–25. AUAI Press; 2019. p. 367–77.

Real E, Aggarwal A, Huang Y, Le QV. Regularized evolution for image classifier architecture search. In: AAAI. 2019. p. 4780–4789.

Liu H, Simonyan K, Vinyals O, Fernando C, Kavukcuoglu K. Hierarchical representations for efficient architecture search. In: 6th International Conference on Learning Representations, ICLR, Vancouver, BC, Canada, April 30 - May 3, Conference Track Proceedings. 2018. OpenReview.net.

Yang Z, Wang Y, Chen X, Shi B, Xu C, Xu C, Tian Q, Xu C. CARS: continuous evolution for efficient neural architecture search. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR, Seattle, WA, USA, June 13-19. IEEE; 2020. p. 1826–835.

Baker B, Gupta O, Naik N, Raskar R. Designing neural network architectures using reinforcement learning. In: 5th International Conference on Learning Representations. ICLR; 2017. OpenReview.net.

Zhong Z, Yang Z, Deng B, Yan J, Wu W, Shao J, Liu C. Blockqnn: Efficient block-wise neural network architecture generation. CoRR. 2018.

Pham H, Guan MY, Zoph B, Le QV, Dean J. Efficient neural architecture search via parameter sharing. In: ICML, vol. 80. 2018. p. 4092–4101.

Cai H, Yang J, Zhang W, Han S, Yu Y. Path-level network transformation for efficient architecture search. In: Proceedings of the 35th International Conference on Machine Learning, ICML, Stockholmsmässan, Stockholm, Sweden, July 10–15. PMLR; 2018. p. 677–86.

Liu C, Zoph B, Neumann M, Shlens J, Hua W, Li L, Fei-Fei L, Yuille AL, Huang J, Murphy K. Progressive neural architecture search. In: Computer Vision - ECCV - 15th European Conference, Munich, Germany, September 8–14, Proceedings, Part I. Springer; 2018.

Perez-Rua J, Baccouche M, Pateux S. Efficient progressive neural architecture search. In: British Machine Vision Conference, BMVC, Newcastle, UK, September 3-6. BMVA Press; 2018. p. 150.

Zhang C, Ren M, Urtasun R. Graph hypernetworks for neural architecture search abs/1810.05749. 2018.

Xie S, Zheng H, Liu C, Lin L. SNAS: stochastic neural architecture search. In: ICLR. 2019.

Dong X, Yang Y. Searching for a robust neural architecture in four GPU hours. In: CVPR. 2019. p. 1761–1770.

Xie S, Girshick RB, Dollár P, Tu Z, He K. Aggregated residual transformations for deep neural networks. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR, Honolulu, HI, USA, July 21–26. IEEE Computer Society; 2017. p. 5987–995.

Zoph B, Vasudevan V, Shlens J, Le QV. Learning transferable architectures for scalable image recognition. In: CVPR. 2018. p. 8697–8710.

留言 (0)

沒有登入
gif