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Iterative Amortized Hierarchical VAE

arXiv 2601.15894 ↗First posted 22 Jan 2026cs.LG cs.AIcited by 0

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Abstract (arXiv)

In this paper we propose the Iterative Amortized Hierarchical Variational Autoencoder (IA-HVAE), which expands on amortized inference with a hybrid scheme containing an initial amortized guess and iterative refinement with decoder gradients. We achieve this by creating a linearly separable decoder in a transform domain (e.g. Fourier space), enabling real-time applications with very high model depths. The architectural change leads to a 35x speed-up for iterative inference with respect to the traditional HVAE. We show that our hybrid approach outperforms fully amortized and fully iterative equivalents in accuracy and speed respectively. Moreover, the IAHVAE shows improved reconstruction quality over a vanilla HVAE in inverse problems such as deblurring and denoising.

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References · 22

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10 more
  1. 2018Iterative Amortized Inferencecited by 94
  2. 2023Posterior Collapse and Latent Variable Non-identifiabilitycited by 57
  3. 2018Distribution Matching in Variational Inferencecited by 35
  4. 2017On the challenges of learning with inference networks on sparse, high-dimensional datacited by 21
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  7. 2025Diffusion models for inverse problemscited by 16
  8. 2023Removing Structured Noise with Diffusion Modelscited by 14
  9. 2024Hierarchical VAE with a Diffusion-based VampPriorcited by 8
  10. 2024Brain-like Variational Inferencecited by 6

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