I have watched Stanford's lectures about artificial intelligence, I currently have one question: why don't we use autoencoders instead of GANs?
Basically, what GAN does is it receives a random vector and generates a new sample from it. So, if we train autoencoders, for example, on cats vs dogs dataset, and then cut off the decoder part and then input random noise vector, wouldn't it do the same job?