提出可实时迭代优化的分层变分自编码器，提升图像重建速度与质量。
Iterative Amortized Hierarchical VAE
- 结合初始快速推断与梯度迭代修正，实现高效推理
- 相比传统方法提速35倍，且在去模糊/去噪任务中更优
- 适合需要高精度与实时性的逆问题重建场景
本文提出迭代摊销分层变分自编码器（IA-HVAE），通过在变换域（如傅里叶空间）构建线性可分离解码器，实现初始摊销预测与解码器梯度迭代优化的混合推理机制。该结构支持深度模型的实时应用，使迭代推断速度相较传统HVAE提升35倍。实验表明，该方法在准确率上优于完全摊销模型，在速度上优于完全迭代模型。同时，在去模糊、去噪等逆问题中，IA-HVAE的重构质量显著优于标准HVAE。
原文摘要 · Abstract (English)
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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