神经网络(十五）标准化流(normalizing flow) 与INN (Invertible Neural Networks)

论文推荐：
L. Dinh, D. Krueger, and Y. Bengio, “NICE: Non-linear Independent Components Estimation,” in ICLR Workshop, 2015
L. Dinh, J. Sohl-Dickstein, and S. Bengio, “Density Estima-tion using Real NVP,” in ICLR, 2017.
D. P. Kingma and P. Dhariwal, “Glow: Generative flow with invertible 1x1 convolutions,” in Advances in Neural Information Processing Systems, 2018
J. Ho, X. Chen, A. Srinivas, Y. Duan, and P. Abbeel, “Flow++: Improving flow-based generative models with variational dequantization and architecture design,” in Proceedings of the 36th International Conference on Machine Learning, ICML, 2019.
J. Behrmann, D. Duvenaud, and J.-H. Jacobsen, “Invertible residual networks,” in Proceedings of the 36th International Conference on Machine Learning, ICML, 2019.
C.-W. Huang, D. Krueger, A. Lacoste, and A. Courville, “Neural Autoregressive Flows,” in ICML, 2018
W. Grathwohl, R. T. Q Chen, J. Bettencourt, I. Sutskever, and D. Duvenaud, “FFJORD: Free-form continuous dynamics for scalable reversible generative models,” in ICLR, 2019

其它参考资料
[1] Normalizing Flows: An Introduction and Review of Current Methods
[2] Introduction to Normalizing Flows (ECCV2020 Tutorial and cvpr 2021 tutorial) by Marcus Brubaker
[3] LiLian Weng’s blog https://lilianweng.github.io/lil-log/...
[4] https://github.com/janosh/awesome-nor...