State-of-the-art anomalous sound detection systems often utilize angular
margin losses to learn suitable representations of acoustic data using an
auxiliary task, which usually is a supervised or self-supervised classification
task. The underlying idea is that, in order to solve this auxiliary task,
specific information about normal data needs to be captured in the learned
representations and that this information is also sufficient to differentiate
between normal and anomalous samples. Especially in noisy conditions,
discriminative models based on angular margin losses tend to significantly
outperform systems based on generative or one-class models. The goal of this
work is to investigate why using angular margin losses with auxiliary tasks
works well for detecting anomalous sounds. To this end, it is shown, both
theoretically and experimentally, that minimizing angular margin losses also
minimizes compactness loss while inherently preventing learning trivial
solutions. Furthermore, multiple experiments are conducted to show that using a
related classification task as an auxiliary task teaches the model to learn
representations suitable for detecting anomalous sounds in noisy conditions.
Among these experiments are performance evaluations, visualizing the embedding
space with t-SNE and visualizing the input representations with respect to the
anomaly score using randomized input sampling for explanation.

通过实验证明，最小化角度边缘损失还可以最小化紧凑性损失，从而避免学习平凡的解决方案，同时还能教会模型适用于在嘈杂条件下检测异常声音的表示方法。

为什么角边缘损失对半监督异常声音检测很有效？

Why do Angular Margin Losses work well for Semi-Supervised Anomalous  Sound Detection?

When formulated as an unsupervised learning problem, anomaly detection often
requires a model to learn the distribution of normal data. Previous works apply
Generative Adversarial Networks (GANs) to anomaly detection tasks and show good
performances from these models. Motivated by the observation that GAN ensembles
often outperform single GANs in generation tasks, we propose to construct GAN
ensembles for anomaly detection. In the proposed method, a group of generators
and a group of discriminators are trained together, so every generator gets
feedback from multiple discriminators, and vice versa. Compared to a single
GAN, a GAN ensemble can better model the distribution of normal data and thus
better detect anomalies. Our theoretical analysis of GANs and GAN ensembles
explains the role of a GAN discriminator in anomaly detection. In the empirical
study, we evaluate ensembles constructed from four types of base models, and
the results show that these ensembles clearly outperform single models in a
series of tasks of anomaly detection.

使用生成对抗网络集成的方法提高无监督学习下的异常检测性能，通过实验验证集成的优越性。

用 GAN 集成方法进行异常检测

GAN Ensemble for Anomaly Detection

Deep autoencoder has been extensively used for anomaly detection. Training on
the normal data, the autoencoder is expected to produce higher reconstruction
error for the abnormal inputs than the normal ones, which is adopted as a
criterion for identifying anomalies. However, this assumption does not always
hold in practice. It has been observed that sometimes the autoencoder
"generalizes" so well that it can also reconstruct anomalies well, leading to
the miss detection of anomalies. To mitigate this drawback for autoencoder
based anomaly detector, we propose to augment the autoencoder with a memory
module and develop an improved autoencoder called memory-augmented autoencoder,
i.e. MemAE. Given an input, MemAE firstly obtains the encoding from the encoder
and then uses it as a query to retrieve the most relevant memory items for
reconstruction. At the training stage, the memory contents are updated and are
encouraged to represent the prototypical elements of the normal data. At the
test stage, the learned memory will be fixed, and the reconstruction is
obtained from a few selected memory records of the normal data. The
reconstruction will thus tend to be close to a normal sample. Thus the
reconstructed errors on anomalies will be strengthened for anomaly detection.
MemAE is free of assumptions on the data type and thus general to be applied to
different tasks. Experiments on various datasets prove the excellent
generalization and high effectiveness of the proposed MemAE.

提出了一种新的改进的自编码器 —— 记忆增强自编码器（MemAE），它通过一个内存模块来防止在异常检测中出现误检。实验表明，MemAE 在不同数据集上均拥有较好的适用性和有效性。