用策略搜索方法训练离散VAE，实现高效图像重建。
Discrete Variational Autoencoding via Policy Search
- 用非参数编码器的自然梯度更新参数化编码器，无需重参数化
- 在ImageNet上用紧凑潜空间实现高质量图像重建
- 适合需要高效离散表示的高维数据任务
变分自编码器中的离散潜在瓶颈具有高比特效率，可用自回归离散分布建模，支持基于Transformer的参数高效多模态搜索。然而，离散随机变量无法精确可微参数化，因此离散VAE通常依赖近似方法，如Gumbel-Softmax重参数化或直通梯度估计，或采用高方差无梯度方法（如REINFORCE），在高维任务（如图像重构）中表现有限。受策略搜索技术启发，我们提出一种离散VAE的训练框架，利用非参数编码器的自然梯度更新参数化编码器，无需重参数化。该方法结合自动步长调整与基于Transformer的编码器，可扩展至ImageNet等挑战性数据集，在紧凑潜空间下重建高维数据的表现优于近似重参数化方法和基于量化的离散自编码器。
原文摘要 · Abstract (English)
Discrete latent bottlenecks in variational autoencoders (VAEs) offer high bit efficiency and can be modeled with autoregressive discrete distributions, enabling parameter-efficient multimodal search with transformers. However, discrete random variables do not allow for exact differentiable parameterization; therefore, discrete VAEs typically rely on approximations, such as Gumbel-Softmax reparameterization or straight-through gradient estimates, or employ high-variance gradient-free methods such as REINFORCE that have had limited success on high-dimensional tasks such as image reconstruction. Inspired by popular techniques in policy search, we propose a training framework for discrete VAEs that leverages the natural gradient of a non-parametric encoder to update the parametric encoder without requiring reparameterization. Our method, combined with automatic step size adaptation and a transformer-based encoder, scales to challenging datasets such as ImageNet and outperforms both approximate reparameterization methods and quantization-based discrete autoencoders in reconstructing high-dimensional data from compact latent spaces.
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