Update README.md

Author: alawryaguila

README.md

@@ -1,19 +1,27 @@
+<div align="center">
+<img src="https://github.com/alawryaguila/multi-view-AE/blob/master/docs/figures/logo.png" width="500px">
+
+# Multi-modal representation learning using autoencoders
+
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-# Multi-view-AE: Multi-modal representation learning using autoencoders
-<p align="center">
-  <img src="https://github.com/alawryaguila/multi-view-AE/blob/master/docs/figures/logo.png" width="600px"></center>
-</p>
+
+</div>
 
 `multi-view-AE` is a collection of multi-modal autoencoder models for learning joint representations from multiple modalities of data. The package is structured such that all models have `fit`, `predict_latents` and `predict_reconstruction` methods. All models are built in Pytorch and Pytorch-Lightning. 
 
 For more information on implemented models and how to use the package, please see the [documentation](https://multi-view-ae.readthedocs.io/en/latest/).
 
+## Library schematic
+<p align="center">
+  <img src="https://github.com/alawryaguila/multi-view-AE/blob/master/docs/figures/schematic_diagram.png" width="800px"></center>
+</p>
+
 ## Models Implemented
 
 Below is a table with the models contained within this repository and links to the original papers.
@@ -22,10 +30,9 @@ Below is a table with the models contained within this repository and links to t
 |:------------:|:-------------------------------------------------------------------------------------------:|:----------------:|:-----------:|
 | mcVAE        | Multi-Channel Variational Autoencoder (mcVAE)                                               | >=1              |[link](http://proceedings.mlr.press/v97/antelmi19a.html)|
 | AE           | Multi-view Autoencoder                                                                      |   >=1            |               |
-| AAE          | Multi-view Adversarial Autoencoder with separate latent representations                     |    >=1           |               |
+| mAAE         | Multi-view Adversarial Autoencoder                                                          |    >=1           |               |
 | DVCCA        | Deep Variational CCA                                                                        |    2             |[link](https://arxiv.org/abs/1610.03454)|
-| jointAAE     | Multi-view Adversarial Autoencoder with joint latent representation                         |   >=1            |               |
-| wAAE         | Multi-view Adversarial Autoencoder with joint latent representation and wasserstein loss    |    >=1           |               |
+| mWAE         | Multi-view Adversarial Autoencoder with a wasserstein loss                                  |    >=1           |               |
 | mmVAE        | Variational mixture-of-experts autoencoder (MMVAE)                                          |   >=1            |[link](https://arxiv.org/abs/1911.03393)|
 | mVAE         | Multimodal Variational Autoencoder (MVAE)                                                   |    >=1           |[link](https://arxiv.org/abs/1802.05335)|
 | me_mVAE      | Multimodal Variational Autoencoder (MVAE) with separate ELBO terms for each view            |    >=1           |[link](https://arxiv.org/abs/1802.05335)|