Variational autoencoders & unets: resources and discussion

Part 2 2022/23
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Variational autoencoders (VAEs) and unets are an important component of Stable Diffusion. Use this thread for asking questions, sharing links, or chatting about stuff related to these topics.

Here’s a post about VAEs from @irhumshafkat: https://towardsdatascience.com/intuitively-understanding-variational-autoencoders-1bfe67eb5daf

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I was watching some youtube videos like this one and still having quite a vague understanding, but @irhumshafkat 's blog gave a very intuitive explanation about the reconstruction loss and regularization loss.

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Thanks for sharing this. It’s an amazingly clear and well written post

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IIRC he was still at high school when he wrote this! :smiley:

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that is amazing.

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This is a good video for intuition in my opinion: Variational Autoencoders - EXPLAINED by CodeEmporium

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Also a good blog post that explains Variational Autoencoders vs GANs in an intuitive way

Variational Autoencoders Explained by Kevin Frans 2016.

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I found this really accessible and detailed. An Introduction to VAEs

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A very detailed, yet mostly informal introduction to Encoder-Decoder architectures: Understanding Variational Autoencoders (VAEs) | by Joseph Rocca | Towards Data Science.

The article is quite long (by Medium standards), and with good reasons.

Rocca is, in general, one of my favourite authors.

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