DE-DARTS: Neural architecture search with dynamic exploration
Tài liệu tham khảo
Elsken, 2019, Neural architecture search: A survey, J. Mach. Learn. Res., 20, 1997
Ren, 2020
Zhong, 2020, Blockqnn: Efficient block-wise neural network architecture generation, IEEE Trans. Pattern Anal. Mach. Intell.
Tan, 2021, RelativeNAS: relative neural architecture search via slow-fast learning, IEEE Trans. Neural Netw. Learn. Syst.
B. Zoph, V. Vasudevan, J. Shlens, Q.V. Le, Learning transferable architectures for scalable image recognition, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018, pp. 8697–8710.
Liu, 2018
H. Pham, M. Guan, B. Zoph, Q. Le, J. Dean, Efficient Neural Architecture Search via Parameters Sharing, in: International Conference on Machine Learning, 2018, pp. 4095–4104.
Liang, 2019
X. Chen, L. Xie, J. Wu, Q. Tian, Progressive differentiable architecture search: Bridging the depth gap between search and evaluation, in: Proceedings of the IEEE International Conference on Computer Vision, 2019, pp. 1294–1303.
Z. Wu, T. Nagarajan, A. Kumar, S. Rennie, L.S. Davis, K. Grauman, R. Feris, Blockdrop: Dynamic inference paths in residual networks, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018, pp. 8817–8826.
L. Liu, J. Deng, Dynamic deep neural networks: Optimizing accuracy-efficiency trade-offs by selective execution, in: Thirty-Second AAAI Conference on Artificial Intelligence, 2018.
E. Real, A. Aggarwal, Y. Huang, Q.V. Le, Regularized evolution for image classifier architecture search, in: Proceedings of the Aaai Conference on Artificial Intelligence, vol. 33, 2019, pp. 4780–4789.
C. Liu, B. Zoph, M. Neumann, J. Shlens, W. Hua, L.-J. Li, L. Fei-Fei, A. Yuille, J. Huang, K. Murphy, Progressive neural architecture search, in: Proceedings of the European Conference on Computer Vision (ECCV), 2018, pp. 19–34.
Xie, 2018
DeVries, 2017
G. Huang, Z. Liu, L. Van Der Maaten, K.Q. Weinberger, Densely connected convolutional networks, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017, pp. 4700–4708.
Cai, 2018
Chang, 2019, DATA: Differentiable ArchiTecture approximation, 874
Xu, 2019
Goyal, 2017
C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, Z. Wojna, Rethinking the inception architecture for computer vision, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016, pp. 2818–2826.
C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, A. Rabinovich, Going deeper with convolutions, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015, pp. 1–9.
Howard, 2017
N. Ma, X. Zhang, H.-T. Zheng, J. Sun, Shufflenet v2: Practical guidelines for efficient cnn architecture design, in: Proceedings of the European Conference on Computer Vision (ECCV), 2018, pp. 116–131.