Critique 1

The page is informative, concise and does a good job of covering stackGAN for targeted generation. Some of my suggestions to improve the page:

A little elaboration of the KL-divergence regularization might be required
Some more results from the papers would have been nice
Mathematical formalism is lacking, though that's because you assume prior knowledge of GAN.

The topic is relevant for the course. 5
The writing is clear and the English is good. 5
The page is written at an appropriate level for CPSC 522 students (where the students have diverse backgrounds). 5
The formalism (definitions, mathematics) was well chosen to make the page easier to understand. 4
The abstract is a concise and clear summary. 5
There were appropriate (original) examples that helped make the topic clear. 4
There was appropriate use of (pseudo-) code. 4
It had a good coverage of representations, semantics, inference, and learning (as appropriate for the topic). 5
It is correct. 5
It was neither too short nor too long for the topic. 4
It was an appropriate unit for a page (it shouldn't be split into different topics or merged with another page). 5
It links to appropriate other pages in the wiki. 5
The references and links to external pages are well chosen. 5
I would recommend this page to someone who wanted to find out about the topic. 5
This page should be highlighted as an exemplary page for others to emulate. 4

If I was grading it out of 20, I would give it: 18