[kahoon]

> Also, what does "disentangling factors of variation" mean?

Let's say you have a neural network that takes an array of pixels and outputs an array of 10 numbers. This encoding array can be used to reconstruct the image using another neural network.

Now you have a difficult time making sense of what those 10 values represent in your encoding array. By the technique of "disentangling factors of variation" you are able to force your networks to assign specific meaning to a range of indices in your encoding array. I.e.: if you are encoding the image of the car, the first 5 values might represent the body of the car (BMW, Audi, Mercedes have different shapes), and 5 values the rest (pose, lightning, colour).