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VAEs and GANs: Implicitly Approximating Complex Distributions with Simple Base Distributions and Deep Neural Networks -- Principles, Necessity, and Limitations
- This tutorial focuses on the architectures of Variational Autoencoders (VAE) and Generative Adversarial Networks (GAN).
- Both VAE and GAN utilize simple distributions, such as Gaussians, as a basis.
- They leverage the nonlinear transformation capabilities of neural networks to approximate complex data distributions.
- The choice of a simple latent prior introduces limitations.
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