Likelihood is a standard estimate for outlier detection. The specific role of the normalization constraint is to ensure that the out-of-distribution (OOD) regime has a small likelihood when samples are learned using maximum likelihood. Because autoencoders do not possess such a process of normalization, they often fail to recognize outliers even when they are obviously OOD. We propose the Normalized Autoencoder (NAE), a normalized probabilistic model constructed from an autoencoder. The probability density of NAE is defined using the reconstruction error of an autoencoder, which is differently defined in the conventional energy-based model. In our model, normalization is enforced by suppressing the reconstruction of negative samples, significantly improving the outlier detection performance. Our experimental results confirm the efficacy of NAE, both in detecting outliers and in generating in-distribution samples.

翻译：隐蔽性是用于外部检测的标准估计。 正常化制约的具体作用是确保在利用最大可能性学习样品时,超出分配(OOOD)制度的可能性很小。 由于自动编码器不具备这种正常化过程,即使明显是 OOD,它们也往往无法辨认出异常值。 我们建议采用标准化自动编码器(NAE),这是从自动编码器中构建的正常概率模型。 NAE的概率密度是使用传统能源模型中不同定义的自动编码器重建错误来确定的。 在我们的模型中,通过抑制反向样品的重建,大大改进外向检测性能,实现正常化。我们的实验结果证实了NAE在探测外向和生成分配样本方面的功效。