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A Unifying Review of Deep and Shallow Anomaly Detection

A Unifying Review of Deep and Shallow Anomaly Detection

24 September 2020
Lukas Ruff
Jacob R. Kauffmann
Robert A. Vandermeulen
G. Montavon
Wojciech Samek
Marius Kloft
Thomas G. Dietterich
Klaus-Robert Muller
    UQCV
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Papers citing "A Unifying Review of Deep and Shallow Anomaly Detection"

50 / 226 papers shown
Title
MADCluster: Model-agnostic Anomaly Detection with Self-supervised Clustering Network
MADCluster: Model-agnostic Anomaly Detection with Self-supervised Clustering Network
Sangyong Lee
Subo Hwang
Dohoon Kim
73
0
0
22 May 2025
DConAD: A Differencing-based Contrastive Representation Learning Framework for Time Series Anomaly Detection
DConAD: A Differencing-based Contrastive Representation Learning Framework for Time Series Anomaly Detection
Wentao Zhang
Xinyu Lin
Wenjun Yu
Guangzhen Yao
jingxiang Zhong
Yongbin Li
Renda Han
Songcheng Xu
Hao Shi
Cuicui Luo
AI4TS
81
0
0
19 Apr 2025
Computing High-dimensional Confidence Sets for Arbitrary Distributions
Computing High-dimensional Confidence Sets for Arbitrary Distributions
Chao Gao
Liren Shan
Vaidehi Srinivas
Aravindan Vijayaraghavan
86
1
0
03 Apr 2025
When Unsupervised Domain Adaptation meets One-class Anomaly Detection: Addressing the Two-fold Unsupervised Curse by Leveraging Anomaly Scarcity
When Unsupervised Domain Adaptation meets One-class Anomaly Detection: Addressing the Two-fold Unsupervised Curse by Leveraging Anomaly Scarcity
Nesryne Mejri
Enjie Ghorbel
Anis Kacem
Pavel Chernakov
Niki Maria Foteinopoulou
Djamila Aouada
88
0
0
28 Feb 2025
MAMo: Leveraging Memory and Attention for Monocular Video Depth Estimation
MAMo: Leveraging Memory and Attention for Monocular Video Depth Estimation
R. Yasarla
H. Cai
Jisoo Jeong
Y. Shi
Risheek Garrepalli
Fatih Porikli
MDE
198
17
0
17 Jan 2025
Human Activity Recognition in an Open World
Human Activity Recognition in an Open World
D. Prijatelj
Samuel Grieggs
Jin Huang
Dawei Du
Ameya Shringi
Christopher Funk
Adam Kaufman
Eric Robertson
Walter J. Scheirer University of Notre Dame
114
3
0
17 Jan 2025
Identifying Information from Observations with Uncertainty and Novelty
Identifying Information from Observations with Uncertainty and Novelty
D. Prijatelj
Timothy J. Ireland
Walter J. Scheirer
94
0
0
16 Jan 2025
Deep Positive-Unlabeled Anomaly Detection for Contaminated Unlabeled Data
Deep Positive-Unlabeled Anomaly Detection for Contaminated Unlabeled Data
Hiroshi Takahashi
Tomoharu Iwata
Atsutoshi Kumagai
Yuuki Yamanaka
88
1
0
29 May 2024
AnomalyDINO: Boosting Patch-based Few-shot Anomaly Detection with DINOv2
AnomalyDINO: Boosting Patch-based Few-shot Anomaly Detection with DINOv2
Simon Damm
M. Laszkiewicz
Johannes Lederer
Asja Fischer
69
7
0
23 May 2024
Energy-based Hopfield Boosting for Out-of-Distribution Detection
Energy-based Hopfield Boosting for Out-of-Distribution Detection
Claus Hofmann
Simon Schmid
Bernhard Lehner
Daniel Klotz
Sepp Hochreiter
OODD
82
9
0
14 May 2024
A Closer Look at AUROC and AUPRC under Class Imbalance
A Closer Look at AUROC and AUPRC under Class Imbalance
Matthew B. A. McDermott
Lasse Hyldig Hansen
Haoran Zhang
Giovanni Angelotti
Jack Gallifant
78
32
0
11 Jan 2024
Set Features for Anomaly Detection
Set Features for Anomaly Detection
Niv Cohen
Issar Tzachor
Yedid Hoshen
143
0
0
24 Nov 2023
ScaleNet: An Unsupervised Representation Learning Method for Limited
  Information
ScaleNet: An Unsupervised Representation Learning Method for Limited Information
Huili Huang
M. M. Roozbahani
SSL
88
806
0
03 Oct 2023
Anomaly Detection-Inspired Few-Shot Medical Image Segmentation Through
  Self-Supervision With Supervoxels
Anomaly Detection-Inspired Few-Shot Medical Image Segmentation Through Self-Supervision With Supervoxels
Stine Hansen
Srishti Gautam
Robert Jenssen
Michael C. Kampffmeyer
98
86
0
03 Mar 2022
Generative Adversarial Networks
Generative Adversarial Networks
Gilad Cohen
Raja Giryes
GAN
237
30,089
0
01 Mar 2022
Self-Attentive Classification-Based Anomaly Detection in Unstructured
  Logs
Self-Attentive Classification-Based Anomaly Detection in Unstructured Logs
S. Nedelkoski
Jasmin Bogatinovski
Alexander Acker
Jorge Cardoso
O. Kao
41
129
0
21 Aug 2020
Backpropagated Gradient Representations for Anomaly Detection
Backpropagated Gradient Representations for Anomaly Detection
Gukyeong Kwon
Mohit Prabhushankar
Dogancan Temel
Ghassan AlRegib
58
71
0
18 Jul 2020
CSI: Novelty Detection via Contrastive Learning on Distributionally
  Shifted Instances
CSI: Novelty Detection via Contrastive Learning on Distributionally Shifted Instances
Jihoon Tack
Sangwoo Mo
Jongheon Jeong
Jinwoo Shin
OODD
67
598
0
16 Jul 2020
Interpretable, Multidimensional, Multimodal Anomaly Detection with
  Negative Sampling for Detection of Device Failure
Interpretable, Multidimensional, Multimodal Anomaly Detection with Negative Sampling for Detection of Device Failure
John Sipple
45
53
0
12 Jul 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
113
240
0
10 Jul 2020
Explainable Deep One-Class Classification
Explainable Deep One-Class Classification
Philipp Liznerski
Lukas Ruff
Robert A. Vandermeulen
Billy Joe Franks
Marius Kloft
Klaus-Robert Muller
47
198
0
03 Jul 2020
Manifolds for Unsupervised Visual Anomaly Detection
Manifolds for Unsupervised Visual Anomaly Detection
Louise Naud
Alexander Lavin
DRL
30
6
0
19 Jun 2020
Understanding Anomaly Detection with Deep Invertible Networks through
  Hierarchies of Distributions and Features
Understanding Anomaly Detection with Deep Invertible Networks through Hierarchies of Distributions and Features
R. Schirrmeister
Yuxuan Zhou
T. Ball
Dan Zhang
UQCV
52
88
0
18 Jun 2020
The Clever Hans Effect in Anomaly Detection
The Clever Hans Effect in Anomaly Detection
Jacob R. Kauffmann
Lukas Ruff
G. Montavon
Klaus-Robert Muller
AAML
50
31
0
18 Jun 2020
How Much Can I Trust You? -- Quantifying Uncertainties in Explaining
  Neural Networks
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
80
31
0
16 Jun 2020
Why Normalizing Flows Fail to Detect Out-of-Distribution Data
Why Normalizing Flows Fail to Detect Out-of-Distribution Data
Polina Kirichenko
Pavel Izmailov
A. Wilson
OODD
71
275
0
15 Jun 2020
Generating Artificial Outliers in the Absence of Genuine Ones -- a
  Survey
Generating Artificial Outliers in the Absence of Genuine Ones -- a Survey
Georg Steinbuss
Klemens Böhm
17
8
0
05 Jun 2020
Rethinking Assumptions in Deep Anomaly Detection
Rethinking Assumptions in Deep Anomaly Detection
Lukas Ruff
Robert A. Vandermeulen
Billy Joe Franks
Klaus-Robert Muller
Marius Kloft
61
89
0
30 May 2020
Language Models are Few-Shot Learners
Language Models are Few-Shot Learners
Tom B. Brown
Benjamin Mann
Nick Ryder
Melanie Subbiah
Jared Kaplan
...
Christopher Berner
Sam McCandlish
Alec Radford
Ilya Sutskever
Dario Amodei
BDL
680
41,736
0
28 May 2020
Transformation Based Deep Anomaly Detection in Astronomical Images
Transformation Based Deep Anomaly Detection in Astronomical Images
E. Reyes
P. Estévez
45
12
0
15 May 2020
Classification-Based Anomaly Detection for General Data
Classification-Based Anomaly Detection for General Data
Liron Bergman
Yedid Hoshen
47
350
0
05 May 2020
Interpreting Rate-Distortion of Variational Autoencoder and Using Model
  Uncertainty for Anomaly Detection
Interpreting Rate-Distortion of Variational Autoencoder and Using Model Uncertainty for Anomaly Detection
Seonho Park
George Adosoglou
P. Pardalos
DRL
UQCV
50
16
0
05 May 2020
Unsupervised Lesion Detection via Image Restoration with a Normative
  Prior
Unsupervised Lesion Detection via Image Restoration with a Normative Prior
Xiaoran Chen
Suhang You
K. Tezcan
E. Konukoglu
MedIm
51
137
0
30 Apr 2020
Hybrid Models for Open Set Recognition
Hybrid Models for Open Set Recognition
Hongjie Zhang
Ang Li
Jie Guo
Yanwen Guo
BDL
123
185
0
27 Mar 2020
Explaining Deep Neural Networks and Beyond: A Review of Methods and
  Applications
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
Fast Distance-based Anomaly Detection in Images Using an Inception-like
  Autoencoder
Fast Distance-based Anomaly Detection in Images Using an Inception-like Autoencoder
Natasa Sarafijanovic-Djukic
Jesse Davis
44
25
0
12 Mar 2020
Out-of-Distribution Generalization via Risk Extrapolation (REx)
Out-of-Distribution Generalization via Risk Extrapolation (REx)
David M. Krueger
Ethan Caballero
J. Jacobsen
Amy Zhang
Jonathan Binas
Dinghuai Zhang
Rémi Le Priol
Aaron Courville
OOD
298
931
0
02 Mar 2020
DROCC: Deep Robust One-Class Classification
DROCC: Deep Robust One-Class Classification
Sachin Goyal
Aditi Raghunathan
Moksh Jain
H. Simhadri
Prateek Jain
VLM
57
163
0
28 Feb 2020
Deep Nearest Neighbor Anomaly Detection
Deep Nearest Neighbor Anomaly Detection
Liron Bergman
Niv Cohen
Yedid Hoshen
UQCV
76
160
0
24 Feb 2020
A Simple Framework for Contrastive Learning of Visual Representations
A Simple Framework for Contrastive Learning of Visual Representations
Ting-Li Chen
Simon Kornblith
Mohammad Norouzi
Geoffrey E. Hinton
SSL
333
18,721
0
13 Feb 2020
Weakly-Supervised Disentanglement Without Compromises
Weakly-Supervised Disentanglement Without Compromises
Francesco Locatello
Ben Poole
Gunnar Rätsch
Bernhard Schölkopf
Olivier Bachem
Michael Tschannen
CoGe
OOD
DRL
220
316
0
07 Feb 2020
Simple and Effective Prevention of Mode Collapse in Deep One-Class
  Classification
Simple and Effective Prevention of Mode Collapse in Deep One-Class Classification
Penny Chong
Lukas Ruff
Marius Kloft
Alexander Binder
76
35
0
24 Jan 2020
Safe Robot Navigation via Multi-Modal Anomaly Detection
Safe Robot Navigation via Multi-Modal Anomaly Detection
Lorenz Wellhausen
René Ranftl
Marco Hutter
59
77
0
22 Jan 2020
Anomaly Detection with Density Estimation
Anomaly Detection with Density Estimation
Benjamin Nachman
David Shih
39
219
0
14 Jan 2020
Your Classifier is Secretly an Energy Based Model and You Should Treat
  it Like One
Your Classifier is Secretly an Energy Based Model and You Should Treat it Like One
Will Grathwohl
Kuan-Chieh Wang
J. Jacobsen
David Duvenaud
Mohammad Norouzi
Kevin Swersky
VLM
76
542
0
06 Dec 2019
Normalizing Flows for Probabilistic Modeling and Inference
Normalizing Flows for Probabilistic Modeling and Inference
George Papamakarios
Eric T. Nalisnick
Danilo Jimenez Rezende
S. Mohamed
Balaji Lakshminarayanan
TPM
AI4CE
189
1,687
0
05 Dec 2019
PyTorch: An Imperative Style, High-Performance Deep Learning Library
PyTorch: An Imperative Style, High-Performance Deep Learning Library
Adam Paszke
Sam Gross
Francisco Massa
Adam Lerer
James Bradbury
...
Sasank Chilamkurthy
Benoit Steiner
Lu Fang
Junjie Bai
Soumith Chintala
ODL
384
42,299
0
03 Dec 2019
Novelty Detection Via Blurring
Novelty Detection Via Blurring
Sung-Ik Choi
Sae-Young Chung
UQCV
40
36
0
27 Nov 2019
Attribute Restoration Framework for Anomaly Detection
Attribute Restoration Framework for Anomaly Detection
Chaoqin Huang
Fei Ye
Jinkun Cao
Maosen Li
Ya Zhang
Cewu Lu
93
50
0
25 Nov 2019
Deep Anomaly Detection with Deviation Networks
Deep Anomaly Detection with Deviation Networks
Guansong Pang
Chunhua Shen
Anton Van Den Hengel
65
355
0
19 Nov 2019
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