Even though the information preference property of VAE might suggest that one should always use the full autoregressive models to achieve a better code length / log - likelihood , especially when slow data generation is not a concern , we argue that this information preference property can be exploited to turn the VAE into a powerful representation learning method that gives us fine - grained control over the kind of information that gets included in the learned representation .