论文标题
遗憾的可能性：变异自动编码器的分布外检测评分
Likelihood Regret: An Out-of-Distribution Detection Score For Variational Auto-encoder
论文作者
论文摘要
深度概率生成模型可以对非常高维数据的可能性进行建模。生成建模的重要应用应该是能够通过在可能性上设置阈值来检测分布外（OOD）样本。但是，最近的一些研究表明，在某些情况下，概率生成模型可以在某些类型的OOD样本上分配更高的可能性，从而根据可能性阈值有问题制定OOD检测规则。为了解决这个问题，已经提出了几种针对深层生成模型的OOD检测方法。在本文中，我们观察到，将许多方法应用于基于变异自动编码器（VAE）的生成模型时失败。作为替代方案，我们提出了可能的遗憾，这是VAE的有效OOD得分。我们在现有方法上基准了我们提出的方法，并且经验结果表明，当应用于VAE时，我们的方法获得了最佳的总体OOD检测性能。
Deep probabilistic generative models enable modeling the likelihoods of very high dimensional data. An important application of generative modeling should be the ability to detect out-of-distribution (OOD) samples by setting a threshold on the likelihood. However, some recent studies show that probabilistic generative models can, in some cases, assign higher likelihoods on certain types of OOD samples, making the OOD detection rules based on likelihood threshold problematic. To address this issue, several OOD detection methods have been proposed for deep generative models. In this paper, we make the observation that many of these methods fail when applied to generative models based on Variational Auto-encoders (VAE). As an alternative, we propose Likelihood Regret, an efficient OOD score for VAEs. We benchmark our proposed method over existing approaches, and empirical results suggest that our method obtains the best overall OOD detection performances when applied to VAEs.
