Towards Explaining Anomalies: A Deep Taylor Decomposition of One-Class
Models

Towards Explaining Anomalies: A Deep Taylor Decomposition of One-Class Models

16 May 2018

Jacob R. Kauffmann

Papers citing "Towards Explaining Anomalies: A Deep Taylor Decomposition of One-Class Models"

17 / 17 papers shown

Title
On Root Cause Localization and Anomaly Mitigation through Causal Inference Xiao Han Lu Zhang Yongkai Wu Shuhan Yuan 26 7 0 08 Dec 2022
Explaining Anomalies using Denoising Autoencoders for Financial Tabular Data Timur Sattarov Dayananda Herurkar Jörn Hees 30 8 0 21 Sep 2022
A general-purpose method for applying Explainable AI for Anomaly Detection John Sipple Abdou Youssef 27 14 0 23 Jul 2022
Explainable Intrusion Detection Systems (X-IDS): A Survey of Current Methods, Challenges, and Opportunities Subash Neupane Jesse Ables William Anderson Sudip Mittal Shahram Rahimi I. Banicescu Maria Seale AAML 50 71 0 13 Jul 2022
Self-Supervised Training with Autoencoders for Visual Anomaly Detection Alexander Bauer Shinichi Nakajima Klaus-Robert Müller 21 9 0 23 Jun 2022
Toward Explainable AI for Regression Models S. Letzgus Patrick Wagner Jonas Lederer Wojciech Samek Klaus-Robert Muller G. Montavon XAI 30 63 0 21 Dec 2021
DeepAID: Interpreting and Improving Deep Learning-based Anomaly Detection in Security Applications Dongqi Han Zhiliang Wang Wenqi Chen Ying Zhong Su Wang Han Zhang Jiahai Yang Xingang Shi Xia Yin AAML 21 76 0 23 Sep 2021
This looks more like that: Enhancing Self-Explaining Models by Prototypical Relevance Propagation Srishti Gautam Marina M.-C. Höhne Stine Hansen Robert Jenssen Michael C. Kampffmeyer 27 49 0 27 Aug 2021
Towards Robust Explanations for Deep Neural Networks Ann-Kathrin Dombrowski Christopher J. Anders K. Müller Pan Kessel FAtt 21 63 0 18 Dec 2020
Counterfactual Explanation Based on Gradual Construction for Deep Networks Hong G Jung Sin-Han Kang Hee-Dong Kim Dong-Ok Won Seong-Whan Lee OOD FAtt 19 22 0 05 Aug 2020
How Much Can I Trust You? -- Quantifying Uncertainties in Explaining Neural Networks Kirill Bykov Marina M.-C. Höhne Klaus-Robert Muller Shinichi Nakajima Marius Kloft UQCV FAtt 27 31 0 16 Jun 2020
Rethinking Assumptions in Deep Anomaly Detection Lukas Ruff Robert A. Vandermeulen Billy Joe Franks Klaus-Robert Muller Marius Kloft 14 89 0 30 May 2020
Explaining Deep Neural Networks and Beyond: A Review of Methods and Applications Wojciech Samek G. Montavon Sebastian Lapuschkin Christopher J. Anders K. Müller XAI 44 82 0 17 Mar 2020
Towards Explainable Artificial Intelligence Wojciech Samek K. Müller XAI 32 436 0 26 Sep 2019
Explaining Anomalies Detected by Autoencoders Using SHAP Liat Antwarg Ronnie Mindlin Miller Bracha Shapira Lior Rokach FAtt TDI 16 86 0 06 Mar 2019
Unsupervised Detection and Explanation of Latent-class Contextual Anomalies Jacob R. Kauffmann G. Montavon L. A. Lima Shinichi Nakajima K. Müller Nico Görnitz 13 0 0 29 Jun 2018
Classifying and Segmenting Microscopy Images Using Convolutional Multiple Instance Learning Oren Z. Kraus Lei Jimmy Ba B. Frey 164 392 0 17 Nov 2015