Data transformations (e.g. rotations, reflections, and cropping) play an important role in self-supervised learning. Typically, images are transformed into different views, and neural networks trained on tasks involving these views produce useful feature representations for downstream tasks, including anomaly detection. However, for anomaly detection beyond image data, it is often unclear which transformations to use. Here we present a simple end-to-end procedure for anomaly detection with learnable transformations. The key idea is to embed the transformed data into a semantic space such that the transformed data still resemble their untransformed form, while different transformations are easily distinguishable. Extensive experiments on time series demonstrate that our proposed method outperforms existing approaches in the one-vs.-rest setting and is competitive in the more challenging n-vs.-rest anomaly detection task. On tabular datasets from the medical and cyber-security domains, our method learns domain-specific transformations and detects anomalies more accurately than previous work.
Detecting Anomalies within Time Series using Local Neural Transformations
University of Stuttgart（斯图加特大学）；Bosch Center for Artificial Intelligence（博世人工智能中心）；TU Kaiserslautern（凯泽斯劳滕工业大学）；University of Southampton（南安普顿大学）；UC Irvine（加州大学欧文分校）
Domain Adaptive and Fine-grained Anomaly Detection for Single-cell Sequencing Data and Beyond
School of Statistics and Mathematics, Zhongnan University of Economics and Law（中南财经政法大学统计与数学学院）；School of Information Engineering, Zhongnan University of Economics and Law（中南财经政法大学信息工程学院）；iWudao Tech（悟道科技）
Anomaly-Aware Vision-Language Adapters for Zero-Shot Anomaly Detection
Dept. of Engineering for Innovation Medicine, University of Verona, Italy（创新医学工程系，威尼斯大学，意大利）；School of Computer Science and Engineering, Beihang University, China（计算机科学与工程学院，北航大学，中国）；Dept. of Computer Science, Reykjavik University, Iceland（计算机科学系，雷克雅未克大学，冰岛）
GDformer: Going Beyond Subsequence Isolation for Multivariate Time Series Anomaly Detection
The Hong Kong University of Science and Technology (Guangzhou)（香港科学与技术大学（广州））；China Mobile (Jiangxi) Virtual Reality Technology Co., Ltd.（中国移动（江西）虚拟现实技术有限公司）；Salesforce AI Research（Salesforce AI研究）；Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）
Isolation-based Spherical Ensemble Representations for Tabular Anomaly Detection
Tsinghua Shenzhen International Graduate School, Tsinghua University, China（清华大学深圳国际研究生院，清华大学，中国）；School of Computing and Information Technology, Great Bay University, China（计算机与信息科技学院，Great Bay大学，中国）；School of Compuer Science, Nanjing University, China（计算机科学学院，南京大学，中国）；Computer department, Dongguan university of technology, China（计算机系，东莞科技学院，中国）；College of Computer Science and Technology, China University of Petroleum, China（计算机科学与技术学院，中国石油大学，中国）；School of IT, Deakin University, Australia（信息技术学院，德金大学，澳大利亚）
Beyond a Single Perspective: Text Anomaly Detection with Multi-View Language Representations
School of Information and Communication Technology, Griffith University, Australia（信息与通信技术学院，格里菲斯大学，澳大利亚）；School of Computer, Electronics and Information, Guangxi University, China（计算机、电子与信息学院，广西大学，中国）
Unravelling physics beyond the standard model with classical and quantum anomaly detection
IBM Research（IBM研究院）；ETH Zürich（苏黎世联邦理工学院）；Autonomous University of Barcelona（巴塞罗那自治大学）；European Organization for Nuclear Research (CERN)（欧洲核子研究组织（CERN））；PASQAL SAS（PASQAL SAS公司）
Benchmarking Anomaly Detection Algorithms: Deep Learning and Beyond
Department of CS Don Bosco College of Engineering Goa, India（印度果阿大学计算机科学系Don Bosco学院工程学院）；Department of Computer Science and Information Systems (CSIS), BITS Pilani K. K. Birla Goa Campus, Goa, India（印度果阿BITS Pilani K. K. Birla校区计算机科学与信息系统系）
