4.4. 参考文献

待处理

正确的文献引用格式

[1] Radford A, Metz L, Chintala S. Unsupervised representation learning with deep convolutional generative adversarial networks[J]. arXiv preprint arXiv:1511.06434, 2015.

[2] PyTorch Official Website. DCGAN Tutorial – PyTorch Tutorial documentation [EB/OL]. https://pytorch.org/tutorials/beginner/dcgan_faces_tutorial.html , 2022-05-07/2023-01-30.

[3] Dumoulin V, Visin F. A guide to convolution arithmetic for deep learning[J]. arXiv preprint arXiv:1603.07285, 2016.

[4] Zhang A, Lipton Z C, Li M, et al. Dive into deep learning[J]. arXiv preprint arXiv:2106.11342, 2021.

[5] Paszke A, Gross S, Massa F, et al. Pytorch: An imperative style, high-performance deep learning library[J]. Advances in neural information processing systems, 2019, 32.

[6] Xu Z, Tao D, Zhang Y, et al. Architectural style classification using multinomial latent logistic regression[C]//Computer Vision–ECCV 2014: 13th European Conference, Zurich, Switzerland, September 6-12, 2014, Proceedings, Part I 13. Springer International Publishing, 2014: 600-615.

[7] Müller R, Kornblith S, Hinton G E. When does label smoothing help?[J]. Advances in neural information processing systems, 2019, 32.

[8] Heusel M, Ramsauer H, Unterthiner T, et al. Gans trained by a two time-scale update rule converge to a local nash equilibrium[J]. Advances in neural information processing systems, 2017, 30.

[9] Salimans T, Goodfellow I, Zaremba W, et al. Improved techniques for training gans[J]. Advances in neural information processing systems, 2016, 29.

[10] Mao X, Li Q, Xie H, et al. Least squares generative adversarial networks[C]//Proceedings of the IEEE international conference on computer vision. 2017: 2794-2802.

[11] Kaggle. architectural-styles-dataset[EB/OL]. https://www.kaggle.com/datasets/dumitrux/architectural-styles-dataset, 2023-02-0901/2023-02-09.