What can I do with an autoencoder?

Question

I cannot find information in detail about autoencoder

What can I do with an autoencoder (and how can I do this), practically speaking?

What does the encoder (this part I think I understand) and a decoder (could not find much about this) part do? Can it for example show on an explainable way how patterns in the data are being represented?

I read some papers that say that it can be used to denoise the input, how does this work? (Am I changing the values of my input)

Is it true that an autoencoder can be also done with PCA (if we assume linearity)?

Answer 1

An autoencoder learns to compress data, and then to decompress it again, recovering the original data.

It does this by learning a mapping from the original feature space to a lower-dimensional space, and then another mapping back. This is indeed, like PCA. The same technique is used to compress JPEG images to transmit them over the web.

Autoencoders can denoise an image because the model will not be able to easily compress or decompress random noise, and so learns to ignore it.

Autoencoders can also be used to find embeddings of data that are likely to have semantic meaning. For example, you can use an autoencoder to compress English text, and a different one to compress French text. If you add some special constraints, then the models can learn to embed in the same lower-dimensional space. This technique underlies many of the recent advances in machine translation.