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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: [ Ссылка ]
✅ VQ-VAE2 paper: [ Ссылка ]
✅ PyTorch code: [ Ссылка ]
✅ ELBO explained: [ Ссылка ]
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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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Huge thank you to these AI Epiphany patreons:
Eli Mahler
Petar Veličković
Zvonimir Sabljic
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💡 The AI Epiphany is a channel dedicated to simplifying the field of AI using creative visualizations and in general, a stronger focus on geometrical and visual intuition, rather than the algebraic and numerical "intuition".
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#vqvae #discretelatents #generativemodeling
