Reimagining Anomalies: What If Anomalies Were Normal?

22 February 2024

Papers citing "Reimagining Anomalies: What If Anomalies Were Normal?"

23 / 23 papers shown

Title
Deep Anomaly Detection on Tennessee Eastman Process Data Fabian Hartung Billy Joe Franks Tobias Michels Dennis Wagner Philipp Liznerski ... Stephan Mandt Michael Bortz Jakob Burger Hans Hasse Marius Kloft 39 6 0 10 Mar 2023
Generative Adversarial Networks Gilad Cohen Raja Giryes GAN 243 30,108 0 01 Mar 2022
CFLOW-AD: Real-Time Unsupervised Anomaly Detection with Localization via Conditional Normalizing Flows Denis A. Gudovskiy Shun Ishizaka Kazuki Kozuka 94 401 0 27 Jul 2021
Towards Total Recall in Industrial Anomaly Detection Karsten Roth Latha Pemula J. Zepeda Bernhard Schölkopf Thomas Brox Peter V. Gehler UQCV 80 907 0 15 Jun 2021
Explaining the Black-box Smoothly- A Counterfactual Approach Junyu Chen Yong Du Yufan He W. Paul Segars Ye Li MedIm FAtt 99 101 0 11 Jan 2021
PaDiM: a Patch Distribution Modeling Framework for Anomaly Detection and Localization Thomas Defard Aleksandr Setkov Angélique Loesch Romaric Audigier UQCV 75 837 0 17 Nov 2020
PANDA: Adapting Pretrained Features for Anomaly Detection and Segmentation Tal Reiss Niv Cohen Liron Bergman Yedid Hoshen 61 251 0 12 Oct 2020
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 104 797 0 24 Sep 2020
Large image datasets: A pyrrhic win for computer vision? Vinay Uday Prabhu Abeba Birhane 57 366 0 24 Jun 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 105 83 0 17 Mar 2020
Evaluating Saliency Map Explanations for Convolutional Neural Networks: A User Study Ahmed Alqaraawi M. Schuessler Philipp Weiß Enrico Costanza N. Bianchi-Berthouze AAML FAtt XAI 61 200 0 03 Feb 2020
Towards Visually Explaining Variational Autoencoders Wenqian Liu Runze Li Meng Zheng Srikrishna Karanam Ziyan Wu B. Bhanu Richard J. Radke Mario Sznaier 92 217 0 18 Nov 2019
Deep Semi-Supervised Anomaly Detection Lukas Ruff Robert A. Vandermeulen Nico Görnitz Alexander Binder Emmanuel Müller K. Müller Marius Kloft UQCV 54 545 0 06 Jun 2019
Deep Anomaly Detection with Outlier Exposure Dan Hendrycks Mantas Mazeika Thomas G. Dietterich OODD 177 1,476 0 11 Dec 2018
Deep Anomaly Detection Using Geometric Transformations I. Golan Ran El-Yaniv 86 607 0 28 May 2018
Explanations based on the Missing: Towards Contrastive Explanations with Pertinent Negatives Amit Dhurandhar Pin-Yu Chen Ronny Luss Chun-Chen Tu Pai-Shun Ting Karthikeyan Shanmugam Payel Das FAtt 107 589 0 21 Feb 2018
Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPR Sandra Wachter Brent Mittelstadt Chris Russell MLAU 98 2,350 0 01 Nov 2017
Modulating early visual processing by language H. D. Vries Florian Strub Jérémie Mary Hugo Larochelle Olivier Pietquin Aaron Courville 106 489 0 02 Jul 2017
Methods for Interpreting and Understanding Deep Neural Networks G. Montavon Wojciech Samek K. Müller FaML 278 2,262 0 24 Jun 2017
Interpretable Explanations of Black Boxes by Meaningful Perturbation Ruth C. Fong Andrea Vedaldi FAtt AAML 74 1,518 0 11 Apr 2017
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization Ramprasaath R. Selvaraju Michael Cogswell Abhishek Das Ramakrishna Vedantam Devi Parikh Dhruv Batra FAtt 268 19,929 0 07 Oct 2016
Wide Residual Networks Sergey Zagoruyko N. Komodakis 326 7,980 0 23 May 2016
Going Deeper with Convolutions Christian Szegedy Wei Liu Yangqing Jia P. Sermanet Scott E. Reed Dragomir Anguelov D. Erhan Vincent Vanhoucke Andrew Rabinovich 422 43,635 0 17 Sep 2014