VQ-VAEs: Neural Discrete Representation Learning | Paper + PyTorch Code Explained

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In this video I cover VQ-VAEs papers:
1) Neural Discrete Representation Learning
2) Generating Diverse High-Fidelity Images with VQ-VAE-2 (the only difference is the existence of a hierarchical structure of latents and priors)

Many novel interesting AI papers such as DALL-E and Jukebox from OpenAI as well as VQ-GAN build off of VQ-VAEs, so it's fairly important to have a good grasp of how they work.

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✅ VQ-VAE1 paper: https://arxiv.org/abs/1711.00937
✅ VQ-VAE2 paper: https://arxiv.org/abs/1906.00446
✅ PyTorch code: https://colab.research.google.com/git...
✅ ELBO explained: https://mbernste.github.io/posts/elbo/

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⌚️ Timetable:
00:00 Intro
01:10 A tangent on autoencoders and VAEs
07:50 Motivation behind discrete representations
08:25 High-level explanation of VQ-VAE framework
11:20 Diving deeper
13:05 VQ-VAE loss
16:20 PyTorch implementation
23:30 KL term missing
25:50 Prior autoregressive models
28:50 Results
32:20 VQ-VAE two

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#vqvae #discretelatents #generativemodeling