This paper introduces anomalib, a novel library for unsupervised anomaly detection and localization. With reproducibility and modularity in mind, this open-source library provides algorithms from the literature and a set of tools to design custom anomaly detection algorithms via a plug-and-play approach. Anomalib comprises state-of-the-art anomaly detection algorithms that achieve top performance on the benchmarks and that can be used off-the-shelf. In addition, the library provides components to design custom algorithms that could be tailored towards specific needs. Additional tools, including experiment trackers, visualizers, and hyper-parameter optimizers, make it simple to design and implement anomaly detection models. The library also supports OpenVINO model optimization and quantization for real-time deployment. Overall, anomalib is an extensive library for the design, implementation, and deployment of unsupervised anomaly detection models from data to the edge.
Universidad de Zaragoza（萨拉戈萨大学）；Hiberus（Hiberus公司）
This paper presents a new Python library for anomaly detection in unsupervised learning approaches. The input for the library is a univariate time series representing observations of a given phenomenon. Then, it can identify anomalous epochs, i.e., time intervals where the observations are above a given percentile of a baseline distribution, defined by a dissimilarity metric. Using time-evolving graphs for the anomaly detection, the library leverages valuable information given by the inter-dependencies among data. Currently, the library implements 28 different dissimilarity metrics, and it has…
We introduce Merlion, an open-source machine learning library for time series. It features a unified interface for many commonly used models and datasets for anomaly detection and forecasting on both univariate and multivariate time series, along with standard pre/post-processing layers. It has several modules to improve ease-of-use, including visualization, anomaly score calibration to improve interpetability, AutoML for hyperparameter tuning and model selection, and model ensembling. Merlion also provides a unique evaluation framework that simulates the live deployment and re-training of a m…
Software and System logs record runtime information about processes executing within a system. These logs have become the most critical and ubiquitous forms of observability data that help developers understand system behavior, monitor system health and resolve issues. However, the volume of logs generated can be humongous (of the order of petabytes per day) especially for complex distributed systems, such as cloud, search engine, social media, etc. This has propelled a lot of research on developing AI-based log based analytics and intelligence solutions that can process huge volume of raw log…
Timeseria: an object-oriented time series processing library
Italian National Center For HPC, Big Data and Quantum Computing（意大利高性能计算、大数据与量子计算国家中心）；INAF - Italian National Institute for Astrophysics（意大利国家天体物理研究所）；University of Trieste（的里雅斯特大学）
LUT University（拉彭兰塔-拉赫蒂工业大学）；Centre Tecnològic de Telecomunicacions de Catalunya (CTTC/CERCA)（加泰罗尼亚电信技术中心（CTTC/CERCA））；Aalborg University（奥尔堡大学）；The American College of Greece（希腊美国学院）
Timely and accurate detection of anomalies in power electronics is becoming increasingly critical for maintaining complex production systems. Robust and explainable strategies help decrease system downtime and preempt or mitigate infrastructure cyberattacks. This work begins by explaining the types of uncertainty present in current datasets and machine learning algorithm outputs. Three techniques for combating these uncertainties are then introduced and analyzed. We further present two anomaly detection and classification approaches, namely the Matrix Profile algorithm and anomaly transformer,…
Columbia University（哥伦比亚大学）；University of Chicago（芝加哥大学）
Data representation plays a critical role in the performance of novelty detection (or ``anomaly detection'') methods in machine learning. The data representation of network traffic often determines the effectiveness of these models as much as the model itself. The wide range of novel events that network operators need to detect (e.g., attacks, malware, new applications, changes in traffic demands) introduces the possibility for a broad range of possible models and data representations. In each scenario, practitioners must spend significant effort extracting and engineering features that are mo…
aeon: a Python toolkit for learning from time series
University of Southampton（南安普顿大学）；Université de Haute-Alsace（上阿尔萨斯大学）；ENSTA Paris（巴黎国立高等先进技术学院）；University of East Anglia（东英吉利大学）；University of Córdoba（科尔多瓦大学）；Humboldt-Universität zu Berlin（柏林洪堡大学）
Distributional Drift Detection in Medical Imaging with Sketching and Fine-Tuned Transformer
Department of Neurology, University of Miami, FL USA（迈阿密大学神经病学系）；Department of Computer Science & IDSC, University of Miami, FL USA（迈阿密大学计算机科学与IDSC系）；Georgetown University Medical Center, Washington D.C. USA（乔治城大学医学中心）
AnomalyClaw: A Universal Visual Anomaly Detection Agent via Tool-Grounded Refutation
Department of Computer Science and Engineering, Southern University of Science and Technology (SUSTech), Shenzhen, China（南方科技大学计算机科学与工程系，深圳，中国）；School of EEE, Nanyang Technological University (NTU), Singapore（南洋理工大学电子工程学院，新加坡）；CFAR, Agency for Science, Technology and Research (A*STAR), Singapore（科技研究局（A*STAR）的CFAR，新加坡）
