Dive into the second part of a comprehensive three-part video series on VQGAN, focusing on Variational AutoEncoders (VAE) and Vector Quantized VAE (VQ-VAE). Explore the limitations of traditional autoencoders and how VAEs address these issues. Gain a deep understanding of VQ-VAE, including its introduction, key concepts, and the crucial role of codebooks in vector quantization. Learn how codebooks are created and their features, with a thorough summary of VQ-VAE principles. Access GitHub resources for hands-on practice and benefit from a detailed recap to solidify your knowledge of these advanced machine learning concepts.
All Things VQGAN - Variational AutoEncoder and VQ-VAE with Codebook Explanations - Part 2
Overview
Syllabus
- Topic Introduction
- Part 1/3 Recap
- Part 2/3 Content
- VAE Introduction
- Two issues with AutoEncoder
- Ho.w VAE solve both the problems
- VAE Summary
- Why do we need VQ-VAE?
- VQ-VAE Intro
- Understanding VQ-VAE
- Understanding Codebook in VQ
- How codebook is created?
- VQ-VAE Summary
- Features inside the Codebook
- VQ-VAE Summary complete
- GitHub Resources
- Recap and Conclusion
Taught by
Prodramp
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All Things VQGAN - VQ-VAE and GAN with Transformers and Codebook Interaction
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All Things VQGAN - AutoEncoder with Latent Space and Reconstruction Error - Part 1
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Understanding How Large Language Models Generate Images - From Autoencoders to Multimodal LLMs
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VQ-VAEs - Neural Discrete Representation Learning - Paper + PyTorch Code Explained
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VQ-GAN - PyTorch Implementation
