====== Generative Adversarial Networks ======
===== Papers =====
  * [[https://arxiv.org/pdf/1706.00550.pdf|Hu et al 2017 - On Unifying Deep Generative Models]] Unifies GANs and Variational Autoencoders (VAEs) with the sleep-wake algorithm, and improves upon them.  See Eric's slides in his course [[http://www.cs.cmu.edu/~epxing/Class/10708-20/lectures.html]] Feb 24 and 26 for more details.
  * [[https://arxiv.org/pdf/1701.04862.pdf|Arjovsky & Bottou 2017 - Towards Principled Methods for Training Generative Adversarial Networks]]
  * WGANs: [[http://proceedings.mlr.press/v70/arjovsky17a/arjovsky17a.pdf|Arjovsky et al 2017 - Wasserstein Generative Adversarial Networks]]

===== GANs in NLP =====
  * [[https://openreview.net/pdf?id=hRk7bo7ZmDw|Kumar & Tsvetkov 2020 - End-to-End Differentiable GANs for Text Generation]]
  * [[https://aclanthology.org/2020.acl-main.191.pdf|Croce et al 2020 - GAN-BERT: Generative Adversarial Learning for Robust Text Classification with a Bunch of Labeled Examples]]
  * [[https://arxiv.org/pdf/2106.08484.pdf|2021 - Generative Conversational Networks]]