1. 毕业设计（论文）的内容和要求
内容：（1）从复杂神经网络的行为和每一层的学习特征出发，建立深度学习C神经网络的数学解析和相关计算。
（2）从数学的角度对深度CNN作解释，参考S Mallat方法基础：将CNN看作是一组级联的线性加权滤波器和非线性函数对数据进行散射。
通过对这组函数的压缩和分离进行分析从而解释深度卷积网络的建模能力。
2. 参考文献
[1] Wengling Chen and James Hays. Sketchygan: towards diverse and realistic sketch to image synthesis. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 94169425, 2018.[2] Taeg Sang Cho, Moshe Butman, Shai Avidan, and William T Freeman. The patch transform and its applications to image editing. In 2008 IEEE Conference on Computer Vision and Pattern Recognition, pages 18. IEEE, 2008. [3] Tali Dekel, Chuang Gan, Dilip Krishnan, Ce Liu, and William T Freeman. Sparse, smart contours to representand edit images. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 35113520, 2018. [4] Tali Dekel, Tomer Michaeli, Michal Irani, and William T Freeman. Revealing and modifying non-local variations in a single image. ACM Transactions on Graphics (TOG),34(6):227, 2015.[5] Xun Huang, Yixuan Li, Omid Poursaeed, John Hopcroft, and Serge Belongie. Stacked generative adversarial networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 50775086, 2017. [6] Nikolay Jetchev, Urs Bergmann, and Roland Vollgraf. Texture synthesis with spatial generative adversarial networks.Workshop on Adversarial Training, NIPS, 2016. [7] Tero Karras, Samuli Laine, and Timo Aila. A style-based generator architecture for generative adversarial networks. arXiv preprint arXiv:1812.04948, 2018.
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