论文标题
部分可观测时空混沌系统的无模型预测
VAEBM: A Symbiosis between Variational Autoencoders and Energy-based Models
论文作者
论文摘要
基于能量的模型（EBM）最近成功地代表了小图像的复杂分布。但是，从它们的采样需要昂贵的马尔可夫链蒙特卡洛（MCMC）迭代，这些迭代在高维像素空间中缓慢混合。与EBM不同，变化自动编码器（VAE）快速生成样品，并配备了潜在空间，可快速遍历数据歧管。但是，VAE倾向于将高概率密度分配给实际数据分布之外的数据空间区域，并且通常无法生成尖锐的图像。在本文中，我们提出了Vaebm，这是VAE和EBM的共生组成，它提供了两全其美的世界。 VAEBM使用最先进的VAE捕获数据分布的整体模式结构，并且它依靠其EBM组件从模型中明确排除非DATA样区域并改进图像样本。此外，VAEBM中的VAE组件使我们能够通过在VAE的潜在空间中对其进行重新聚集来加快MCMC的更新。我们的实验结果表明，VAEBM在几个基准图像数据集上的生成质量上的最先进的VAE和EBM的表现都大。它可以产生高质量的图像，最大为256 $ \ times $ 256的像素，带有短MCMC链。我们还证明VAEBM提供了完整的模式覆盖范围，并且在分布外检测中表现良好。源代码可从https://github.com/nvlabs/vaebm获得
Energy-based models (EBMs) have recently been successful in representing complex distributions of small images. However, sampling from them requires expensive Markov chain Monte Carlo (MCMC) iterations that mix slowly in high dimensional pixel space. Unlike EBMs, variational autoencoders (VAEs) generate samples quickly and are equipped with a latent space that enables fast traversal of the data manifold. However, VAEs tend to assign high probability density to regions in data space outside the actual data distribution and often fail at generating sharp images. In this paper, we propose VAEBM, a symbiotic composition of a VAE and an EBM that offers the best of both worlds. VAEBM captures the overall mode structure of the data distribution using a state-of-the-art VAE and it relies on its EBM component to explicitly exclude non-data-like regions from the model and refine the image samples. Moreover, the VAE component in VAEBM allows us to speed up MCMC updates by reparameterizing them in the VAE's latent space. Our experimental results show that VAEBM outperforms state-of-the-art VAEs and EBMs in generative quality on several benchmark image datasets by a large margin. It can generate high-quality images as large as 256$\times$256 pixels with short MCMC chains. We also demonstrate that VAEBM provides complete mode coverage and performs well in out-of-distribution detection. The source code is available at https://github.com/NVlabs/VAEBM
