Although mainstream unsupervised anomaly detection (AD) algorithms perform
well in academic datasets, their performance is limited in practical
application due to the ideal experimental setting of clean training data.
Training with noisy data is an inevitable problem in real-world anomaly
detection but is seldom discussed. This paper considers label-level noise in
image sensory anomaly detection for the first time. To solve this problem, we
proposed a memory-based unsupervised AD method, SoftPatch, which efficiently
denoises the data at the patch level. Noise discriminators are utilized to
generate outlier scores for patch-level noise elimination before coreset
construction. The scores are then stored in the memory bank to soften the
anomaly detection boundary. Compared with existing methods, SoftPatch maintains
a strong modeling ability of normal data and alleviates the overconfidence
problem in coreset. Comprehensive experiments in various noise scenes
demonstrate that SoftPatch outperforms the state-of-the-art AD methods on the
MVTecAD and BTAD benchmarks and is comparable to those methods under the
setting without noise.

本文首次考虑图像感应异常检测中的标签级噪声问题，提出了一种基于记忆的无监督异常检测方法 SoftPatch，该方法能够有效地去噪，并在补丁级别上生成异常分数以软化异常检测边界，在各种噪声场景下，SoftPatch 在 MVTecAD 和 BTAD 基准测试上表现优于现有方法，在无噪声设置下与其他方法效果相当。

SoftPatch：无监督的带噪数据异常检测

SoftPatch: Unsupervised Anomaly Detection with Noisy Data

Mainstream unsupervised anomaly detection algorithms often excel in academic
datasets, yet their real-world performance is restricted due to the controlled
experimental conditions involving clean training data. Addressing the challenge
of training with noise, a prevalent issue in practical anomaly detection, is
frequently overlooked. In a pioneering endeavor, this study delves into the
realm of label-level noise within sensory time-series anomaly detection (TSAD).
This paper presents a novel and practical end-to-end unsupervised TSAD when the
training data are contaminated with anomalies. The introduced approach, called
TSAD-C, is devoid of access to abnormality labels during the training phase.
TSAD-C encompasses three modules: a Decontaminator to rectify the abnormalities
(aka noise) present in the training data, a Variable Dependency Modeling module
to capture both long-term intra- and inter-variable dependencies within the
decontaminated data that can be considered as a surrogate of the pure normal
data, and an Anomaly Scoring module to detect anomalies. Our extensive
experiments conducted on three widely used physiological datasets conclusively
demonstrate that our approach surpasses existing methodologies, thus
establishing a new state-of-the-art performance in the field.

本研究介绍了一种无监督式的感知时间序列异常检测方法（TSAD-C），通过对带有异常的训练数据进行去噪、建立变量依赖模型和检测异常值等步骤，成功在三个广泛使用的生理数据集上超越现有方法，达到了领域中的最新技术水平。