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Why is the Mahalanobis Distance Effective for Anomaly Detection?

Why is the Mahalanobis Distance Effective for Anomaly Detection?

1 March 2020
Ryo Kamoi
Kei Kobayashi
    OODD
ArXivPDFHTML

Papers citing "Why is the Mahalanobis Distance Effective for Anomaly Detection?"

15 / 15 papers shown
Title
Deciphering the Definition of Adversarial Robustness for post-hoc OOD Detectors
Deciphering the Definition of Adversarial Robustness for post-hoc OOD Detectors
Peter Lorenz
Mario Fernandez
Jens Müller
Ullrich Kothe
AAML
78
1
0
21 Jun 2024
Iterative Deployment Exposure for Unsupervised Out-of-Distribution Detection
Iterative Deployment Exposure for Unsupervised Out-of-Distribution Detection
Lars Doorenbos
Raphael Sznitman
Pablo Márquez-Neila
OODD
33
0
0
04 Jun 2024
Toward Stronger Textual Attack Detectors
Toward Stronger Textual Attack Detectors
Pierre Colombo
Marine Picot
Nathan Noiry
Guillaume Staerman
Pablo Piantanida
59
5
0
21 Oct 2023
Understanding the limitations of self-supervised learning for tabular
  anomaly detection
Understanding the limitations of self-supervised learning for tabular anomaly detection
Kimberly T. Mai
Toby O. Davies
Lewis D. Griffin
SSL
32
0
0
15 Sep 2023
A Benchmark for Out of Distribution Detection in Point Cloud 3D Semantic
  Segmentation
A Benchmark for Out of Distribution Detection in Point Cloud 3D Semantic Segmentation
Lokesh Veeramacheneni
Matias Valdenegro-Toro
3DPC
UQCV
30
2
0
11 Nov 2022
Decomposing Representations for Deterministic Uncertainty Estimation
Decomposing Representations for Deterministic Uncertainty Estimation
Haiwen Huang
Joost R. van Amersfoort
Y. Gal
UQCV
OOD
UD
32
1
0
01 Dec 2021
Data Invariants to Understand Unsupervised Out-of-Distribution Detection
Data Invariants to Understand Unsupervised Out-of-Distribution Detection
Lars Doorenbos
Raphael Sznitman
Pablo Márquez-Neila
OODD
27
6
0
26 Nov 2021
When Neural Networks Using Different Sensors Create Similar Features
When Neural Networks Using Different Sensors Create Similar Features
Hugues Moreau
A. Vassilev
Liming Chen
25
0
0
04 Nov 2021
Transfer Learning Gaussian Anomaly Detection by Fine-tuning
  Representations
Transfer Learning Gaussian Anomaly Detection by Fine-tuning Representations
Oliver Rippel
Arnav Chavan
Chucai Lei
Dorit Merhof
44
18
0
09 Aug 2021
Graceful Degradation and Related Fields
Graceful Degradation and Related Fields
J. Dymond
31
4
0
21 Jun 2021
A Simple Fix to Mahalanobis Distance for Improving Near-OOD Detection
A Simple Fix to Mahalanobis Distance for Improving Near-OOD Detection
Jie Jessie Ren
Stanislav Fort
J. Liu
Abhijit Guha Roy
Shreyas Padhy
Balaji Lakshminarayanan
UQCV
33
216
0
16 Jun 2021
Data augmentation and pre-trained networks for extremely low data
  regimes unsupervised visual inspection
Data augmentation and pre-trained networks for extremely low data regimes unsupervised visual inspection
Pierre Gutierrez
Antoine Cordier
Thais Caldeira
Théophile Sautory
18
4
0
02 Jun 2021
Feature Space Singularity for Out-of-Distribution Detection
Feature Space Singularity for Out-of-Distribution Detection
Haiwen Huang
Zhihan Li
Lulu Wang
Sishuo Chen
Bin Dong
Xinyu Zhou
OODD
22
65
0
30 Nov 2020
Contrastive Training for Improved Out-of-Distribution Detection
Contrastive Training for Improved Out-of-Distribution Detection
Jim Winkens
Rudy Bunel
Abhijit Guha Roy
Robert Stanforth
Vivek Natarajan
...
Alan Karthikesalingam
Simon A. A. Kohl
taylan. cemgil
S. M. Ali Eslami
Olaf Ronneberger
OODD
19
234
0
10 Jul 2020
Adversarial examples in the physical world
Adversarial examples in the physical world
Alexey Kurakin
Ian Goodfellow
Samy Bengio
SILM
AAML
287
5,842
0
08 Jul 2016
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