Answers.Body: <p>There are <strong>many variations</strong> of the original VAE (proposed in the 2013 paper <a href="https://arxiv.org/pdf/1312.6114.pdf" rel="nofollow noreferrer">Auto-Encoding Variational Bayes</a>), with <strong>different purposes</strong> (such as the generation of discrete data or graphs). Of course, I cannot enumerate all of them, so here I will list only the ones I am currently aware of.</p>
  <ul>
  <li><a href="https://papers.nips.cc/paper/2015/file/8d55a249e6baa5c06772297520da2051-Paper.pdf" rel="nofollow noreferrer">Conditional variational auto-encoder (CVAE)</a> (2015, NeurIPS)</li>
  <li><a href="https://research.google/pubs/pub44904/" rel="nofollow noreferrer">Adversarial autoencoders (AAE)</a> (2015, ICLR)</li>
  <li><a href="https://proceedings.neurips.cc/paper/2016/file/6ae07dcb33ec3b7c814df797cbda0f87-Paper.pdf" rel="nofollow noreferrer">Ladder variational autoencoder (LVAE)</a> (2016,  NeurIPS)</li>
  <li><a href="http://proceedings.mlr.press/v48/larsen16.pdf" rel="nofollow noreferrer">VAE/GAN</a> (2016, PMLR)</li>
  <li><a href="https://proceedings.neurips.cc/paper/2017/file/7a98af17e63a0ac09ce2e96d03992fbc-Paper.pdf" rel="nofollow noreferrer">Vector quantised variational autoencoder (VQ-VAE)</a> (2017, NeurIPS)</li>
  <li><a href="https://arxiv.org/pdf/1511.06406.pdf" rel="nofollow noreferrer">Denoising variational autoencoder (DVAE)</a> (2017, AAAI)</li>
  <li><a href="https://arxiv.org/pdf/1703.01925.pdf" rel="nofollow noreferrer">Grammar variational autoencoder (GVAE)</a> (2017, ICML)</li>
  <li><a href="http://www.quantum-machine.org/workshops/nips2017/assets/pdf/sdvae_workshop_camera_ready.pdf" rel="nofollow noreferrer">Syntax-directed variational autoencoder (SD-VAE)</a> (2017, ICLR)</li>
  <li><a href="https://arxiv.org/pdf/1611.05013.pdf" rel="nofollow noreferrer">PixelVAE</a> (2017)</li>
  <li><a href="https://link.springer.com/chapter/10.1007/978-3-030-01418-6_41" rel="nofollow noreferrer">GraphVAE</a> (2018, ICANN)</li>
  <li><a href="https://papers.nips.cc/paper/2018/file/b8a03c5c15fcfa8dae0b03351eb1742f-Paper.pdf" rel="nofollow noreferrer">Constrained graph variational autoencoder (CGVAE)</a> (2018, NeurIPS)</li>
  <li><a href="https://pubs.acs.org/doi/pdf/10.1021/acscentsci.7b00572" rel="nofollow noreferrer">Character VAE (CVAE)</a> (2018, ACS central science)</li>
  <li><a href="https://papers.nips.cc/paper/2018/file/1458e7509aa5f47ecfb92536e7dd1dc7-Paper.pdf" rel="nofollow noreferrer">Regularized VAE (RVAE)</a> (2018, NeurIPS)</li>
  <li><a href="http://proceedings.mlr.press/v80/jin18a.html" rel="nofollow noreferrer">Junction tree variational autoencoder (JT-VAE)</a> (2018, PMLR)</li>
  <li><a href="https://ojs.aaai.org//index.php/AAAI/article/view/4538" rel="nofollow noreferrer">InfoVAE/MMD-VAE</a> (2019, AAAI)</li>
  <li><a href="http://proceedings.mlr.press/v97/kajino19a/kajino19a.pdf" rel="nofollow noreferrer">Molecular hypergraph grammar variational autoencoder (MHG-VAE)</a> (2019, ICML)</li>
  <li><a href="https://papers.nips.cc/paper/2019/file/e205ee2a5de471a70c1fd1b46033a75f-Paper.pdf" rel="nofollow noreferrer">DAG variational autoencoder (D-VAE)</a> (2019, NeurIPS)</li>
  <li><a href="https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=8438540" rel="nofollow noreferrer">Denoising adversarial autoencoders (iDAAE and DAAE)</a> (2019, IEEE Transactions on Neural Networks and Learning Systems)</li>
  <li><a href="https://papers.nips.cc/paper/2019/file/a2186aa7c086b46ad4e8bf81e2a3a19b-Paper.pdf" rel="nofollow noreferrer">MACRo-mIcro Disentangled Variational Auto-Encoder (MacridVAE)</a> (2019, NeurIPS)</li>
  </ul>
  <p>VAEs have been used for <strong>drug design</strong> (see e.g. <a href="http://proceedings.mlr.press/v80/jin18a.html" rel="nofollow noreferrer">JT-VAE</a>) and in <strong>reinforcement learning</strong> (see e.g. <a href="https://papers.nips.cc/paper/2018/hash/2de5d16682c3c35007e4e92982f1a2ba-Abstract.html" rel="nofollow noreferrer">world models</a>). I don't know if they have been used for self-driving cars, but it's possible.</p>
  
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Body: <p>I have been studying about auto-encoders and variational auto-encoders. I would like to know how many variants of VAEs are there today.</p>
  <p>If there are many variants, can they be used for feature extraction for complex reinforcement learning tasks like self-driving cars?</p>
  
Tags: <reinforcement-learning><reference-request><variational-autoencoder>
Title: How many types of variational auto-encoders are there?
UserDisplayName: Himanshu
ThreadText: # How many types of variational auto-encoders are there?
  
  (Asked by: username_0 on 2020-09-20)
  
  I have been studying about auto-encoders and variational auto-encoders. I would like to know how many variants of VAEs are there today.
  
  If there are many variants, can they be used for feature extraction for complex reinforcement learning tasks like self-driving cars?
  
  **Tags:** reinforcement-learning, reference-request, variational-autoencoder
  
  ---
  
  ## 1 answers
  
  ### Answer by username_1 on 2020-11-22. Score: 4
  
  There are **many variations** of the original VAE (proposed in the 2013 paper [Auto-Encoding Variational Bayes](https://arxiv.org/pdf/1312.6114.pdf)), with **different purposes** (such as the generation of discrete data or graphs). Of course, I cannot enumerate all of them, so here I will list only the ones I am currently aware of.
  
  * [Conditional variational auto-encoder (CVAE)](https://papers.nips.cc/paper/2015/file/8d55a249e6baa5c06772297520da2051-Paper.pdf) (2015, NeurIPS)
  * [Adversarial autoencoders (AAE)](https://research.google/pubs/pub44904/) (2015, ICLR)
  * [Ladder variational autoencoder (LVAE)](https://proceedings.neurips.cc/paper/2016/file/6ae07dcb33ec3b7c814df797cbda0f87-Paper.pdf) (2016, NeurIPS)
  * [VAE/GAN](http://proceedings.mlr.press/v48/larsen16.pdf) (2016, PMLR)
  * [Vector quantised variational autoencoder (VQ-VAE)](https://proceedings.neurips.cc/paper/2017/file/7a98af17e63a0ac09ce2e96d03992fbc-Paper.pdf) (2017, NeurIPS)
  * [Denoising variational autoencoder (DVAE)](https://arxiv.org/pdf/1511.06406.pdf) (2017, AAAI)
  * [Grammar variational autoencoder (GVAE)](https://arxiv.org/pdf/1703.01925.pdf) (2017, ICML)
  * [Syntax-directed variational autoencoder (SD-VAE)](http://www.quantum-machine.org/workshops/nips2017/assets/pdf/sdvae_workshop_camera_ready.pdf) (2017, ICLR)
  * [PixelVAE](https://arxiv.org/pdf/1611.05013.pdf) (2017)
  * [GraphVAE](https://link.springer.com/chapter/10.1007/978-3-030-01418-6_41) (2018, ICANN)
  * [Constrained graph variational autoencoder (CGVAE)](https://papers.nips.cc/paper/2018/file/b8a03c5c15fcfa8dae0b03351eb1742f-Paper.pdf) (2018, NeurIPS)
  * [Character VAE (CVAE)](https://pubs.acs.org/doi/pdf/10.1021/acscentsci.7b00572) (2018, ACS central science)
  * [Regularized VAE (RVAE)](https://papers.nips.cc/paper/2018/file/1458e7509aa5f47ecfb92536e7dd1dc7-Paper.pdf) (2018, NeurIPS)
  * [Junction tree variational autoencoder (JT-VAE)](http://proceedings.mlr.press/v80/jin18a.html) (2018, PMLR)
  * [InfoVAE/MMD-VAE](https://ojs.aaai.org//index.php/AAAI/article/view/4538) (2019, AAAI)
  * [Molecular hypergraph grammar variational autoencoder (MHG-VAE)](http://proceedings.mlr.press/v97/kajino19a/kajino19a.pdf) (2019, ICML)
  * [DAG variational autoencoder (D-VAE)](https://papers.nips.cc/paper/2019/file/e205ee2a5de471a70c1fd1b46033a75f-Paper.pdf) (2019, NeurIPS)
  * [Denoising adversarial autoencoders (iDAAE and DAAE)](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=8438540) (2019, IEEE Transactions on Neural Networks and Learning Systems)
  * [MACRo-mIcro Disentangled Variational Auto-Encoder (MacridVAE)](https://papers.nips.cc/paper/2019/file/a2186aa7c086b46ad4e8bf81e2a3a19b-Paper.pdf) (2019, NeurIPS)
  
  VAEs have been used for **drug design** (see e.g. [JT-VAE](http://proceedings.mlr.press/v80/jin18a.html)) and in **reinforcement learning** (see e.g. [world models](https://papers.nips.cc/paper/2018/hash/2de5d16682c3c35007e4e92982f1a2ba-Abstract.html)). I don't know if they have been used for self-driving cars, but it's possible.
  
  > **Comments:**
  > **username_1**: TODO (optional): add links to the implementations.  
  
  ---
  
  