Hello.

I am seeing the GAN specialization course on Coursera by instructor Sharon Zhou.

In one of the lectures, she says that a disadvantage of GANs is that it cannot do density estimation and thus is not useful for anomaly detection. (not sure if you can access it).

In the next video, she says that VAEs don’t have this problem.

I am a little confused about this. Could anyone please explain what she means?

As far as I can understand from the lecture, density estimation means learning how probable/frequent particular features are in a dataset. Like, how probable is it that a dog will have droopy ears. Then, we can use this info to detect anomalies if they do not exhibit these features.

But, isn’t this exactly what GANs learn? aren’t GANs learning to mimic the distribution of the training data?

Also, how is a VAE different in this particular regard?

Could someone please help explain this?

Thank you.

  • mao1756@alien.topBEnglish
    1·
    10 months ago

    I learned GAN recently too so take it with a grain of salt.

    The generative network learns a function that takes random noise as an input and returns a generated sample. As a consequence, the network learns the distribution of the true samples, but that information is hard to retrieve because it is encoded as weights of neural networks. So yes, it learns the distribution, but we cannot use it because it is in a hard-to-use format.