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Should VAEs be even used for non-generative tasks? If I were to use both models for embedding images, how would the embedding space differ on a structural level?

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For the vanilla autoencoder the structure is like this:

enter image description here

It can be treated as a nonlinear extension of PCA, while for the variational autoencoder a mean and a standard deviation is added as a layer for each hidden variable in the middle layer:

enter image description here

For the detailed difference please refer to this answer.

Reference:
Intuitively Understanding Variational Autoencoders

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  • $\begingroup$ Sorry, but this only states what they are. I'm asking for how they differ when it comes to non-generative tasks such as embeddings. $\endgroup$ – Daniel Aug 11 at 22:43
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    $\begingroup$ @Daniel OK. I will update that later. $\endgroup$ – Lerner Zhang Aug 11 at 22:47

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