We propose a new probabilistic method for unsupervised recovery of corrupted data. Given a large ensemble of degraded samples, our method recovers accurate posteriors of clean values, allowing the exploration of the manifold of possible reconstructed data and hence characterising the underlying uncertainty. In this setting, direct application of classical variational methods often gives rise to collapsed densities that do not adequately explore the solution space. Instead, we derive our novel reduced entropy condition approximate inference method that results in rich posteriors. We test our model in a data recovery task under the common setting of missing values and noise, demonstrating superior performance to existing variational methods for imputation and de-noising with different real data sets. We further show higher classification accuracy after imputation, proving the advantage of propagating uncertainty to downstream tasks with our model.

中文翻译：

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断层自动编码器：损坏数据的无监督贝叶斯恢复

我们提出了一种新的概率方法来无监督地恢复损坏的数据。给定大量退化样本，我们的方法恢复了干净值的准确后验，允许探索可能的重建数据的流形，从而表征潜在的不确定性。在这种情况下，直接应用经典变分方法通常会导致无法充分探索解空间的坍缩密度。相反，我们推导出我们新颖的减少熵条件近似推理方法，该方法导致丰富的后验。我们在缺失值和噪声的常见设置下在数据恢复任务中测试我们的模型，证明了对不同真实数据集进行插补和去噪的现有变分方法的优越性能。