论文标题
ID条件的自动编码器，用于无监督的异常检测
ID-Conditioned Auto-Encoder for Unsupervised Anomaly Detection
论文作者
论文摘要
在本文中，我们引入了ID条件的自动编码器，以进行无监督的异常检测。我们的方法是针对开放式识别的类调节自动编码器（C2AE）的改编。假设非反对样本构成不同的ID，我们将有条件的自动编码器与这些ID提供的标签应用。与C2AE相反，我们的方法省略了分类子任务，并将学习过程减少到单次运行。我们通过将恒定向量作为非匹配标签的目标来进一步简化学习过程。我们将我们的方法应用于声音的上下文，以进行机器状况监视。我们从DCASE 2020挑战任务2中评估了ToyAdmos和Mimii数据集的方法。我们进行了消融研究，以指示我们方法的哪些步骤对结果最大。
In this paper, we introduce ID-Conditioned Auto-Encoder for unsupervised anomaly detection. Our method is an adaptation of the Class-Conditioned Auto-Encoder (C2AE) designed for the open-set recognition. Assuming that non-anomalous samples constitute of distinct IDs, we apply Conditioned Auto-Encoder with labels provided by these IDs. Opposed to C2AE, our approach omits the classification subtask and reduces the learning process to the single run. We simplify the learning process further by fixing a constant vector as the target for non-matching labels. We apply our method in the context of sounds for machine condition monitoring. We evaluate our method on the ToyADMOS and MIMII datasets from the DCASE 2020 Challenge Task 2. We conduct an ablation study to indicate which steps of our method influences results the most.
