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.

翻译：本文提出迭代摊销分层变分自编码器（IA-HVAE），该模型通过融合初始摊销估计与基于解码器梯度的迭代优化机制，拓展了摊销推断的框架。我们通过在变换域（如傅里叶空间）构建线性可分离解码器实现该方案，使其能够支持高模型深度的实时应用。该架构改进使迭代推断速度较传统HVAE提升35倍。实验表明，我们的混合方法在精度上优于完全摊销方案，在速度上优于完全迭代方案。此外，在去模糊与去噪等逆问题中，IA-HVAE相比原始HVAE展现出显著提升的重建质量。