Bridging Unsupervised and Semi-Supervised Anomaly Detection: A Theoretically-Grounded and Practical Framework with Synthetic Anomalies

16 June 2025

Papers citing "Bridging Unsupervised and Semi-Supervised Anomaly Detection: A Theoretically-Grounded and Practical Framework with Synthetic Anomalies"

9 / 9 papers shown

Title
NNG-Mix: Improving Semi-supervised Anomaly Detection with Pseudo-anomaly Generation Hao Dong Gaëtan Frusque Yue Zhao Eleni Chatzi Olga Fink AAML 81 7 0 20 Nov 2023
ADBench: Anomaly Detection Benchmark Songqiao Han Xiyang Hu Hailiang Huang Mingqi Jiang Yue Zhao OOD 74 313 0 19 Jun 2022
A Unifying Review of Deep and Shallow Anomaly Detection Lukas Ruff Jacob R. Kauffmann Robert A. Vandermeulen G. Montavon Wojciech Samek Marius Kloft Thomas G. Dietterich Klaus-Robert Muller UQCV 117 803 0 24 Sep 2020
Interpretable, Multidimensional, Multimodal Anomaly Detection with Negative Sampling for Detection of Device Failure John Sipple 50 55 0 12 Jul 2020
DROCC: Deep Robust One-Class Classification Sachin Goyal Aditi Raghunathan Moksh Jain H. Simhadri Prateek Jain VLM 82 166 0 28 Feb 2020
Deep Semi-Supervised Anomaly Detection Lukas Ruff Robert A. Vandermeulen Nico Görnitz Alexander Binder Emmanuel Müller K. Müller Marius Kloft UQCV 58 547 0 06 Jun 2019
Deep Anomaly Detection with Outlier Exposure Dan Hendrycks Mantas Mazeika Thomas G. Dietterich OODD 183 1,487 0 11 Dec 2018
Learning rates for classification with Gaussian kernels Shaobo Lin Jinshan Zeng Xiangyu Chang 34 12 0 28 Feb 2017
Fast learning rates for plug-in classifiers Jean-Yves Audibert Alexandre B. Tsybakov 578 467 0 17 Aug 2007