将VQ-VAE压缩为可高效推断的简化模型
Distillation of a tractable model from the VQ-VAE
- 从VQ-VAE中选取高概率潜在变量构建可计算模型
- 在密度估计与条件生成任务中表现接近原模型
- 适合需要快速推理的生成模型应用
具有离散潜空间的深度生成模型（如向量量化变分自编码器，VQ-VAE）虽具备优异的数据生成能力，但因其潜空间过大，导致概率推断被认为不可行。本文证明可通过选取高概率潜变量子集，将VQ-VAE有效蒸馏为一个可计算的模型。该策略在原模型潜空间利用率较低时尤为高效，这在实践中极为常见。我们将蒸馏后的模型建模为概率电路，既保持了VQ-VAE的表达能力，又实现了可计算的概率推断。实验表明，在密度估计与条件生成任务中性能具有竞争力，挑战了VQ-VAE本质上不可推断的观点。
原文摘要 · Abstract (English)
Deep generative models with discrete latent space, such as the Vector-Quantized Variational Autoencoder (VQ-VAE), offer excellent data generation capabilities, but, due to the large size of their latent space, their probabilistic inference is deemed intractable. We demonstrate that the VQ-VAE can be distilled into a tractable model by selecting a subset of latent variables with high probabilities. This simple strategy is particularly efficient, especially if the VQ-VAE underutilizes its latent space, which is, indeed, very often the case. We frame the distilled model as a probabilistic circuit, and show that it preserves expressiveness of the VQ-VAE while providing tractable probabilistic inference. Experiments illustrate competitive performance in density estimation and conditional generation tasks, challenging the view of the VQ-VAE as an inherently intractable model.
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