I am a student and I am studying machine learning. I am focusing on deep generative models, and in particular to autoencoders and variational autoencoders (VAE).
I am trying to understand the concept, but I am having some problems.
So far, I have understood that an autoencoder takes an input, for example an image, and wants to reduce this image into a latent space, which should contain the underlying features of the dataset, with an operation of encoding, then, with an operation of decoding, it reconstrunct the image which has lost some information due to the encoding part.
After this, with a loss function, it reconstruct the latent space and so get the latent features.
about the VAE, it uses a probabilistic approch, so we have to learn the meand anv covariance of a gaussian.
So far this is what I have understood.
What I have really unclear is what are we trying to learn with autoencoders and VAE?
I have seen examples where an image goes feom a non smiling to a smiling face, or to a black and white image to a colored image.
But I don't understand the main concept, which is: what does an autoencoder do?
I add here some sources of where I studied so that who needs can see them: