Variational autoencoders (VAEs) provide an effective and simple method for modeling complex distributions. However, training VAEs often requires considerable hyperparameter tuning to determine the optimal amount of information retained by the latent variable. We study the impact of calibrated decoders, which learn the uncertainty of the decoding distribution and can determine this amount of information automatically, on the VAE performance. While many methods for learning calibrated decoders have been proposed, many of the recent papers that employ VAEs rely on heuristic hyperparameters and ad-hoc modifications instead. We perform the first comprehensive comparative analysis of calibrated decoder and provide recommendations for simple and effective VAE training. Our analysis covers a range of image and video datasets and several single-image and sequential VAE models. We further propose a simple but novel modification to the commonly used Gaussian decoder, which computes the prediction variance analytically. We observe empirically that using heuristic modifications is not necessary with our method. Project website is at https://orybkin.github.io/sigma-vae/

翻译：虽然提出了多种校准解码器(VAEs)的方法,但许多最近使用VAE的论文都依赖于超光度超分计和临时修改。我们进行第一次校准解码器的综合比较分析,并为简单有效的VAE培训提供建议。我们的分析涵盖一系列图像和视频数据集以及若干单一图像和连续VAE模型。我们进一步建议对常用的高斯解码器进行简单但新颖的修改,该模型对预测差异进行分析。我们从经验上看到,使用高斯解码器不需要使用超光度调整方法。项目网站位于https://orybkin.githubio/gagmas-va。项目网站位于https://orybkin.githubio/gramas-va。项目网站位于https://orybkin.gitubio/gmas-vava。