I'm watching Ian Goodfellow's introduction to generative models.
When he was introducing variational autoencoders at 22:29, he said:
Variational autoencoders are good at obtaining high likelihood, but they tend to produce lower quality samples, and in particular, the samples are relatively blurry.
My questions are:
What does it mean by obtaining a high likelihood? Does it means that the density model that VAE learnt centres around those training samples? Is this the reason that causes VAE to produce low-quality samples?
Why are the generated samples blurry? Is VAE averaging something when it is generating a new sample?
When do we want high likelihood and when do we want high-quality samples?