[doc] added some images in the DR-GAN section

doc/guide_conditionalgan.rst

@@ -66,6 +66,15 @@ and you can generate faces based on this dataset by doing::
   $ ./bin/train_conditionalgan_casia.py
 
 
+Also, the "Improved Wasserstein with gradient penalty" training procedure has been implemented for
+this model. But the first attempt using it does not reach good generation abilities (and you should 
+carefully tune the discriminator learning rate), and seems to be less stable.
+
+.. figure:: img/iw-gp-conditionalgan-iteration-33000.png
+   :align: center
+    
+   Iteration 33000 of the Improved Wasserstein training.
+
 
 
 .. todo:: script that samples from the models

doc/guide_drgan.rst

@@ -37,4 +37,39 @@ The generator model consists in three parts:
    Architecture of the DR-GAN
 
 
+To train the DR-GAN, simply execute the following script:: 
+
+  $ ./bin/train_drgan_multipie.py
+
+To monitor the training, we show a randomly selected real example (left)
+and the corresponding generated images of the supposedly same identity 
+*at a random pose* (right)
+
+.. figure:: img/dr-gan-epoch-1-174.png
+   :align: center
+    
+   Epoch 1: Target pose (left profile) not met
+
+.. figure:: img/dr-gan-epoch-1-80.png
+   :align: center
+    
+   Epoch 1: Target pose is better, but ID is clearly not
+
+.. figure:: img/dr-gan-epoch-10-244.png
+   :align: center
+    
+   Epoch 10: Target pose met, good quality (frontal), but ID problem
+
+.. figure:: img/dr-gan-epoch-50-176.png
+   :align: center
+    
+   Epoch 50: Able to generate view from not frontal image
+
+.. figure:: img/dr-gan-epoch-51-109.png
+   :align: center
+    
+   Epoch 50: Going from one profile to another
+
+
+