[1] |
Goodfellow I J, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, et al. Generative adversarial nets. In: Proceedings of the 27th International Conference on Neural Information Processing Systems. Montreal, Canada: MIT Press, 2014. 2672-2680 |
[2] |
Makhzani A, Shlens J, Jaitly N, Goodfellow I, Frey B. Adversarial autoencoders. arXiv preprint arXiv: 1511. 05644, 2015. |
[3] |
Mao X D, Li Q, Xie H R, Lau R Y K, Wang Z, Smolley S P. Least squares generative adversarial networks. arXiv preprint ArXiv: 1611. 04076, 2016. |
[4] |
Durugkar I, Gemp I, Mahadevan S. Generative multi-adversarial networks. arXiv preprint arXiv: 1611. 01673, 2016. |
[5] |
Huang X, Li Y X, Poursaeed O, Hopcroft J, Belongie1 S. Stacked generative adversarial networks. arXiv preprint arXiv: 1612. 04357, 2016. |
[6] |
Saito M, Matsumoto E, Saito S. Temporal generative adversarial nets with singular value clipping. In: Proceedings of the 2017 IEEE Conference on Computer Vision. Venice, Italy: ICCV, 2017. 2849-2858 |
[7] |
Che T, Li Y R, Zhang R X, Hjelm R D, Li W J, Song Y Q, etal. Maximum-likelihood augmented discrete generative adversarial networks. arXiv preprint arXiv: 1702. 07983, 2017. |
[8] |
王坤峰, 苟超, 段艳杰, 林懿伦, 郑心湖, 王飞跃.生成式对抗网络GAN的研究进展与展望.自动化学报, 2017, 43(3):321-332 http://www.aas.net.cn/CN/abstract/abstract19012.shtml
Wang Kun-Feng, Gou Chao, Duan Yan-Jie, Lin Yi-Lun, Zheng Xin-Hu, Wang Fei-Yue. Generative adversarial networks:the state of the art and beyond. Acta Automatica Sinica, 2017, 43(3):321-332 http://www.aas.net.cn/CN/abstract/abstract19012.shtml |
[9] |
Arjovsky M, Chintala S, Bottou L. Wasserstein GAN. arXiv preprint arXiv: 1701. 07875, 2017. |
[10] |
Donahue J, Krähenbühl P, Darrell T. Adversarial feature learning. arXiv preprint arXiv: 1605. 09782, 2016. |
[11] |
LeCun Y, Huang F. Loss functions for discriminative training of energy-based models. In: Proceedings of the 10th International Workshop on Artificial Intelligence and Statistics. Barbados: AIS, 2005. 206-213 |
[12] |
乔俊飞, 潘广源, 韩红桂.一种连续型深度信念网的设计与应用.自动化学报, 2015, 41(12):2138-2146 http://www.aas.net.cn/CN/abstract/abstract18786.shtml
Qiao Jun-Fei, Pan Guang-Yuan, Han Hong-Gui. Design and application of continuous deep belief network. Acta Automatica Sinica, 2015, 41(12):2138-2146 http://www.aas.net.cn/CN/abstract/abstract18786.shtml |
[13] |
乔俊飞, 王功明, 李晓理, 韩红桂, 柴伟.基于自适应学习率的深度信念网设计与应用.自动化学报, 2017, 43(8):1339-1349 http://www.aas.net.cn/CN/abstract/abstract19108.shtml
Qiao Jun-Fei, Wang Gong-Ming, Li Xiao-Li, Han Hong-Gui, Chai Wei. Design and application of deep belief network with adaptive learning rate. Acta Automatica Sinica, 2017, 43(8):1339-1349 http://www.aas.net.cn/CN/abstract/abstract19108.shtml |
[14] |
Lopes N, Ribeiro B. Towards adaptive learning with improved convergence of deep belief networks on graphics processing units. Pattern Recognition, 2014, 47(1):114-127 doi: 10.1016/j.patcog.2013.06.029 |
[15] |
王功明, 李文静, 乔俊飞.基于PLSR自适应深度信念网络的出水总磷预测.化工学报, 2017, 68(5):1987-1997 http://www.doc88.com/p-6922879556285.html
Wang Gong-Ming, Li Wen-Jing, Qiao Jun-Fei. Prediction of effluent total phosphorus using PLSR-based adaptive deep belief network. CIESC Journal, 2017, 68(5):1987-1997 http://www.doc88.com/p-6922879556285.html |
[16] |
Hinton G E. Training products of experts by minimizing contrastive divergence. Neural Computation, 2002, 14(8):1771-1800 doi: 10.1162/089976602760128018 |
[17] |
Le Roux N, Bengio Y. Representational power of restricted boltzmann machines and deep belief networks. Neural Computation, 2008, 20(6):1631-1649 doi: 10.1162/neco.2008.04-07-510 |
[18] |
Hinton G E, Osindero S, Teh Y W. A fast learning algorithm for deep belief nets. Neural Computation, 2006, 18(7):1527-1554 doi: 10.1162/neco.2006.18.7.1527 |
[19] |
Alain G, Bengio Y. What regularized auto-encoders learn from the data-generating distribution. The Journal of Machine Learning Research, 2014, 15(1):3563-3593 http://jmlr.csail.mit.edu/papers/volume15/alain14a/alain14a.pdf |
[20] |
Chan P P K, Lin Z, Hu X, Tsang E C C, Yeung D S. Sensitivity based robust learning for stacked autoencoder against evasion attack. Neurocomputing, 2017, 267:572-580 doi: 10.1016/j.neucom.2017.06.032 |
[21] |
Huang G B, Chen L, Siew C K. Universal approximation using incremental constructive feedforward networks with random hidden nodes. IEEE Transactions on Neural Networks, 2006, 17(4):879-892 doi: 10.1109/TNN.2006.875977 |
[22] |
Leung F H F, Lam H K, Ling S H, Tam P K S. Tuning of the structure and parameters of a neural network using an improved genetic algorithm. IEEE Transactions on Neural networks, 2003, 14(1):79-88 doi: 10.1109/TNN.2002.804317 |
[23] |
de la Rosa E, Yu W. Randomized algorithms for nonlinear system identification with deep learning modification. Information Sciences, 2016, 364-365:197-212 doi: 10.1016/j.ins.2015.09.048 |
[24] |
Zhao J B, Mathieu M, LeCun Y. Energy-based generative adversarial network. arXiv preprint arXiv: 1609. 03126, 2016. |
[25] |
Larochelle H, Bengio Y, Louradour J, Lamblin P. Exploring strategies for training deep neural networks. The Journal of Machine Learning Research, 2009, 10:1-40 http://www.cs.toronto.edu/~larocheh/publications/jmlr-larochelle09a.pdf |
[26] |
Wang Y, Wang X G, Liu W Y. Unsupervised local deep feature for image recognition. Information Sciences, 2016, 351:67-75 doi: 10.1016/j.ins.2016.02.044 |
[27] |
Qi G J. Loss-sensitive generative adversarial networks on lipschitz densities. arXiv preprint arXiv: 1701. 06264, 2017. |
[28] |
Yang J W, Kannan A, Batra D, Parikh D. LR-GAN: layered recursive generative adversarial networks for image generation. arXiv preprint arXiv: 1703. 01560, 2017. |
[29] |
Saatchi Y, Wilson A. Bayesian GAN. arXiv preprint arXiv: 1705. 09558, 2017. |
[30] |
Hinton G E, Srivastava N, Krizhevsky A, Sutskever I, Salakhutdinov R R. Improving neural networks by preventing co-adaptation of feature detectors. arXiv preprint arXiv: 1207. 0580, 2012. |
[31] |
Xu B, Wang N Y, Chen T Q, Li M. Empirical evaluation of rectified activations in convolutional network. arXiv preprint arXiv: 1505. 00853, 2015. |
[32] |
Goroshin R, Bruna J, Tompson J, Eigen D, LeCun Y. Unsupervised learning of spatiotemporally coherent metrics. In: Proceedings of the 2015 IEEE Conference on Computer Vision (ICCV). Santiago, Chile: IEEE, 2015. 4086-4093 |
[33] |
Metz L, Poole B, Pfau D, Sohl-Dickstein J. Unrolled generative adversarial networks. arXiv preprint arXiv: 1611. 02163, 2016. |
[34] |
Springenberg J T. Unsupervised and semi-supervised learning with categorical generative adversarial networks. arXiv preprint arXiv: 1511. 06390, 2015. |