deep-generative-models

My own implementation on various types of deep generative models

Variational Auto Encoder (VAE)

• D. P. Kingma and M. Welling. "Auto-Encoding Variational Bayes", In ICLR, 2014.

Variational Deep Embedding: An Unsupervised and Generative Approach to Clustering (VaDE)

• Z. Jiang, Y. Zheng, H. Tan, B. Tang, and H. Zhou, "Variational Deep Embedding: An Unsupervised and Generative Approach to Clustering", In International Joint Conference on Artificial Intelligence, 2017.

InfoGAN

• X. Chen, Y. Duan, R. Houthooft, J. Schulman, I. Sutskever, and P. Abbeel. "InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets". In NIPS, 2016.

Info-WassersteinGAN with Gradient Penalty and Consistency Term regularizations (Info-WGAN-GP-CT)

• I. Gulrajani, F. Ahmed, M. Arjovsky, V. Dumoulin, and A. C. Courville, "Improved Training of Wasserstein GANs", In NIPS, 2017.
• X. Wei, Z. Liu, L. Wang, and B. Gong, "Improving the Improved Training of Wasserstein GANs: A Consistency Term and Its Dual Effect", International Conference on Learning Representations (ICLR), 2018.