Fully automatic CNN design with inception and ResNet blocks
Author:
Funder
Nazarbayev University
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence,Software
Link
https://link.springer.com/content/pdf/10.1007/s00521-022-07700-9.pdf
Reference23 articles.
1. Sun Y, Xue B, Zhang M, Yen GG (2020) Completely automated CNN architecture design based on blocks. IEEE Trans Neural Netw Learn Syst 31(4):1242–1254
2. Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A (2015) Going deeper with convolutions. In: IEEE conference on computer vision and pattern recognition (CVPR) 2015, pp 1–9
3. He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: IEEE conference on computer vision and pattern recognition (CVPR) 2016, pp 770–778
4. Huang G, Liu Z, Van Der Maaten L, Weinberger KQ (2017) Densely connected convolutional networks. In: IEEE conference on computer vision and pattern recognition (CVPR) 2017, pp 2261–2269
5. Zoph B, Le QV (2016) Neural architecture search with reinforcement learning. CoRR, vol. abs/1611.01578 [Online]. arXiv:1611.01578
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