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葛道辉 等:轻量级神经网络架构综述 2651
[40] Wang P, Hu Q, Zhang Y, Zhang C, Liu Y, Cheng J. Two-Step quantization for low-bit neural networks. In: Proc. of the IEEE Conf.
on Computer Vision and Pattern Recognition. 2018. 4376−4384.
[41] Tung F, Mori G. CLIP-Q: Deep network compression learning by in-parallel pruning-quantization. In: Proc. of the IEEE Conf. on
Computer Vision and Pattern Recognition. 2018. 7873−7882.
[42] Denton EL, Zaremba W, Bruna J, Cun YL, Fergus R. Exploiting linear structure within convolutional networks for efficient
evaluation. In: Proc. of the Advances in Neural Information Processing Systems. 2014. 1269−1277.
[43] Zhang X, Zou J, He K, Sun J. Accelerating very deep convolutional networks for classification and detection. IEEE Trans. on
Pattern Analysis and Machine Intelligence, 2015,38(10):1943−1955.
[44] Lebedev V, Ganin Y, Rakhuba M, Oseledets I, Lempitsky V. Speeding-Up convolutional neural networks using fine-tuned cp-
decomposition. In: Proc. of the 3rd Int’l Conf. on Learning Representations. 2015.
[45] Kim YD, Park E, Yoo S, Choi T, Yang L, Shin D. Compression of deep convolutional neural networks for fast and low power
mobile applications. In: Proc. of the 4th Int’l Conf. on Learning Representations. 2016.
[46] Hinton G, Vinyals O, Dean J. Distilling the knowledge in a neural network. In: Proc. of the Advances in Neural Information
Processing Systems Workshop, Vol.27. 2014.
[47] Romero A, Ballas N, Kahou SE, Chassang A, Gatta C, Bengio Y. Fitnets: Hints for thin deep nets. In: Proc. of the 3rd Int’l Conf.
on Learning Representations. 2015.
[48] Zagoruyko S, Komodakis N. Paying more attention to attention: Improving the performance of convolutional neural networks via
attention transfer. In: Proc. of the 5th Int’l Conf. on Learning Representations. 2017.
[49] Lillicrap TP, Hunt JJ, Pritzel A, Heess N, Erez T, Tassa Y, Wierstra D. Continuous control with deep reinforcement learning. In:
Proc. of the 4th Int’l Conf. on Learning Representations. 2016.
[50] Ashok A, Rhinehart N, Beainy F, Kitani KM. N2N learning: Network to network compression via policy gradient reinforcement
learning. In: Proc. of the 6th Int’l Conf. on Learning Representations. 2018.
[51] Wong C, Houlsby N, Lu Y, Gesmundo A. Transfer learning with neural AutoML. In: Proc. of the Advances in Neural Information
Processing Systems, Vol.31. 2018. 8366−8375.
[52] Lin J, Rao Y, Lu J, Zhou J. Runtime neural pruning. In: Proc. of the Advances in Neural Information Processing Systems. 2017.
2181−2191.
[53] Wang H, Zhang Q, Wang Y, Hu H. Structured probabilistic pruning for convolutional neural network acceleration. In: Proc. of the
British Machine Vision Conf. 2018.
[54] Real E, Aggarwal A, Huang Y, Le QV. Regularized evolution for image classifier architecture search. In: Proc. of the AAAI Conf.
on Artificial Intelligence. 2019.
[55] Chen LC, Collins M, Zhu Y, Papandreou G, Zoph B, Schroff F, Adam H, Shlens J. Searching for efficient multi-scale architectures
for dense image prediction. In: Proc. of the Advances in Neural Information Processing Systems. 2018. 8699−8710.
[56] Chollet F. Xception: Deep learning with depth-wise separable convolutions. In: Proc. of the IEEE Conf. on Computer Vision and
Pattern Recognition. 2017. 1251−1258.
[57] Yu F, Koltun V. Multi-Scale context aggregation by dilated convolutions. In: Proc. of the 4th Int’l Conf. on Learning
Representations. 2016.
[58] Baker B, Gupta O, Naik N, Raskar R. Designing neural network architectures using reinforcement learning. In: Proc. of the 5th Int’l
Conf. on Learning Representations. 2017.
[59] Suganuma M, Shirakawa S, Nagao T. A genetic programming approach to designing convolutional neural network architectures. In:
Proc. of the Genetic and Evolutionary Computation Conf. 2017. 497−504.
[60] Cai H, Chen T, Zhang W, Yu Y, Wang J. Efficient architecture search by network transformation. In: Proc. of the 32nd AAAI Conf.
on Artificial Intelligence. 2018.
[61] Mendoza H, Klein A, Feurer M, Springenberg J, Hutter F. Towards automatically-tuned neural networks. In: Proc. of the Workshop
on Automatic Machine Learning. 2016. 58−65.
[62] Zoph B, Le QV. Neural architecture search with reinforcement learning. In: Proc. of the 5th Int’l Conf. on Learning
Representations. 2017.
[63] Szegedy C, Liu W, Jia Y, Sermanet P, Reed RE, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A. Going deeper with
convolutions. In: Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition. 2015. 1−9.
[64] Liu C, Zoph B, Neumann M, Shlens J, Hua W, Li LJ, Fei L, Yuille AL, Huang J, Murphy K. Progressive neural architecture search.
In: Proc. of the European Conf. on Computer Vision. 2018. 19−34.