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Hierarchical VAE with a Diffusion-based VampPrior
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Deep hierarchical variational autoencoders (VAEs) are powerful latent variable generative models. In this paper, we introduce Hierarchical VAE with Diffusion-based Variational Mixture of the Posterior Prior (VampPrior). We apply amortization to scale the VampPrior to models with many stochastic layers. The proposed approach allows us to achieve better performance compared to the original VampPrior work and other deep hierarchical VAEs, while using fewer parameters. We empirically validate our method on standard benchmark datasets (MNIST, OMNIGLOT, CIFAR10) and demonstrate improved training stability and latent space utilization.
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Cited by 1 Pith paper
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On Designing Diffusion Autoencoders for Efficient Generation and Representation Learning
Small binary latents conditioned via cross-attention let a diffusion autoencoder generate from a uniform Bernoulli prior with fewer steps while keeping representation quality.
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