Anomaly Detector

Anomaly detection aims to identify data points deviating from expected patterns, crucial for various applications like predictive maintenance and cybersecurity. Current research emphasizes improving the accuracy and explainability of anomaly detection, focusing on models like autoencoders, variational autoencoders, and recurrent neural networks (RNNs), often incorporating techniques such as dynamic time warping and counterfactual explanations. These advancements are driving progress in diverse fields, enabling more robust systems for fault diagnosis, fraud detection, and improved safety in critical infrastructure.

Papers

January 7, 2023

How to Allocate your Label Budget? Choosing between Active Learning and Learning to Reject in Anomaly Detection
Lorenzo Perini, Daniele Giannuzzi, Jesse Davis
LeArning Abstract Anomaly Detection Active Learning Unlabeled Anomaly Decision Boundary Risky Prompt Rejection Anomaly Detector Label Budget

November 24, 2022

Meta-Learning for Automated Selection of Anomaly Detectors for Semi-Supervised Datasets
David Schubert, Pritha Gupta, Marcel Wever
Anomaly Detection Unlabeled Anomaly Anomaly Detector Confusion Probability

September 15, 2022

Self-Supervised Texture Image Anomaly Detection By Fusing Normalizing Flow and Dictionary Learning
Yaohua Guo, Lijuan Song, Zirui Ma
Anomaly Detection Normalizing Flow Dictionary Learning Anomaly Detector Anomaly Detection Framework Industrial Image Anomaly Detection Texture Anomaly

April 26, 2022

Novel Applications for VAE-based Anomaly Detection Systems
Luca Bergamin, Tommaso Carraro, Mirko Polato, Fabio Aiolli
Anomaly Detection Deep Generative Model Variational Auto Anomaly Detector Novel Application

March 25, 2022

Learning to Adapt to Unseen Abnormal Activities under Weak Supervision
Jaeyoo Park, Junha Kim, Bohyung Han
LeArning Abstract Anomaly Detection Weak Supervision Video Annotation Anomaly Detector Meta Learning Framework

February 25, 2022

Stacked Residuals of Dynamic Layers for Time Series Anomaly Detection
L. Zancato, A. Achille, G. Paolini, A. Chiuso, S. Soatto
Anomaly Detection Multivariate Time Series Residual Model Differentiable Neural Anomaly Detector Anomaly Detection Benchmark Dynamic Layer

February 16, 2022

Latent Outlier Exposure for Anomaly Detection with Contaminated Data
Chen Qiu, Aodong Li, Marius Kloft, Maja Rudolph, Stephan Mandt
Anomaly Detection Unlabeled Anomaly Anomalous Data Outlier Exposure Anomaly Detector Contaminated Data

November 29, 2021

Anomaly-Aware Semantic Segmentation by Leveraging Synthetic-Unknown Data
Guan-Rong Lu, Yueh-Cheng Liu, Tung-I Chen, Hung-Ting Su, Tsung-Han Wu, Winston H. Hsu
Semantic Segmentation Anomaly Detection Synthetic Data Anomaly Segmentation Anomaly Detector Anomaly Aware Semantic Segmentation