[36] Yingzhen Li, Richard E Turner, and Qiang Liu. Approximate inference with amortised mcmc. arXiv preprint arXiv:1702.08343, 2017.
[37] Jianwen Xie, Yang Lu, Ruiqi Gao, and Ying Nian Wu. Cooperative learning of energy-based model and latent variable model via mcmc teaching. In Proceedings of AAAI Conference on Artificial Intelligence (AAAI), 2018.
[38] Francisco Ruiz and Michalis Titsias. A contrastive divergence for combining variational inference and mcmc. In Proceedings of International Conference on Machine Learning (ICML), 2019.
[39] Thomas M Cover. Elements of information theory. John Wiley & Sons, 1999.
[40] Jonathan Ho and Tim Salimans. Classifier-free diffusion guidance. In NeurIPS 2021 Workshop on Deep Generative Models and Downstream Applications.
[41] Zhisheng Xiao and Tian Han. Adaptive multi-stage density ratio estimation for learning latent space energy-based model. Advances in Neural Information Processing Systems (NeurIPS), 2022.
[42] Yann LeCun. The mnist database of handwritten digits. http://yann.lecun.com/exdb/mnist/, 1998.
[43] Yuval Netzer, Tao Wang, Adam Coates, Alessandro Bissacco, Bo Wu, and Andrew Y Ng. Reading digits in natural images with unsupervised feature learning. 2011.
[44] Ziwei Liu, Ping Luo, Xiaogang Wang, and Xiaou Tang. Deep learning face attributes in the wild. In Proceedings of the IEEE international conference on computer vision, pages 3730-3738, 2015.
[45] Alex Krizhevsky et al. Learning multiple layers of features from tiny images. 2009.
[46] Cheng-Han Lee, Ziwei Liu, Lingyun Wu, and Ping Luo. Maskgan: Towards diverse and interactive facial image manipulation. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2020.
[47] Fisher Yu, Ari Seff, Yinda Zhang, Shuran Song, Thomas Funkhouser, and Jianxiong Xiao. Lsun: Construction of a large-scale image dataset using deep learning with humans in the loop. arXiv preprint arXiv:1506.03365, 2015.
[48] Bin Dai and David Wipf. Diagnosing and enhancing vae models. International Conference on Learning Representations (ICLR), 2019.
[49] Partha Ghosh, Mehdi SM Sajjadi, Antonio Vergari, Michael Black, and Bernhard Schölkopf. From variational to deterministic autoencoders. International Conference on Learning Representations (ICLR), 2020.
[50] Jyoti Aneja, Alex Schwing, Jan Kautz, and Arash Vahdat. A contrastive learning approach for training variational autoencoder priors. Advances in Neural Information Processing Systems (NeurIPS), 2021.
[51] Tian Han, Yang Lu, Song-Chun Zhu, and Ying Nian Wu. Alternating back-propagation for generator network. In Proceedings of AAAI Conference on Artificial Intelligence (AAAI), 2017.
[52] Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter. Gans trained by a two time-scale update rule converge to a local nash equilibrium. Advances in Neural Information Processing Systems (NeurIPS), 2017.
[53] Rameen Abdal, Yipeng Qin, and Peter Wonka. Image2stylegan: How to embed images into the stylegan latent space? In Proceedings of International Conference on Computer Vision (ICCV), 2019.
[54] Weihao Xia, Yulun Zhang, Yujiu Yang, Jing-Hao Xue, Bolei Zhou, and Ming-Hsuan Yang. Gan inversion: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022.