论文标题
ESAD：端到端深半监督异常检测
ESAD: End-to-end Deep Semi-supervised Anomaly Detection
论文作者
论文摘要
本文探讨了半监督的异常检测，这是一个更实用的异常检测设置，其中提供了一小部分标记样品集。我们提出了一个新的基于KL-Divergence的目标函数，以用于半监督的异常检测，并表明两个因素：数据和潜在表示之间的相互信息以及潜在表示的熵，构成了异常检测的积分目标函数。为了解决同时优化这两个因素的矛盾，我们提出了一种新颖的编码器编码器结构，第一个编码器专注于优化互信息，第二个编码器专注于优化熵。这两个编码器被迫共享类似的编码，并对其潜在表示一致。广泛的实验表明，该提出的方法在多个基准数据集上明显胜过几个最先进的方法，包括医学诊断和几种经典的异常检测基准。
This paper explores semi-supervised anomaly detection, a more practical setting for anomaly detection where a small additional set of labeled samples are provided. We propose a new KL-divergence based objective function for semi-supervised anomaly detection, and show that two factors: the mutual information between the data and latent representations, and the entropy of latent representations, constitute an integral objective function for anomaly detection. To resolve the contradiction in simultaneously optimizing the two factors, we propose a novel encoder-decoder-encoder structure, with the first encoder focusing on optimizing the mutual information and the second encoder focusing on optimizing the entropy. The two encoders are enforced to share similar encoding with a consistent constraint on their latent representations. Extensive experiments have revealed that the proposed method significantly outperforms several state-of-the-arts on multiple benchmark datasets, including medical diagnosis and several classic anomaly detection benchmarks.
