ResearchTrend.AI
  • Papers
  • Communities
  • Events
  • Blog
  • Pricing
Papers
Communities
Social Events
Terms and Conditions
Pricing
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 1908.04388
  4. Cited By
Detecting semantic anomalies
v1v2v3 (latest)

Detecting semantic anomalies

13 August 2019
Faruk Ahmed
Aaron Courville
ArXiv (abs)PDFHTML

Papers citing "Detecting semantic anomalies"

49 / 49 papers shown
Title
Odd-One-Out: Anomaly Detection by Comparing with Neighbors
Odd-One-Out: Anomaly Detection by Comparing with Neighbors
A. Bhunia
Changjian Li
Hakan Bilen
148
0
0
28 Jun 2024
Looking 3D: Anomaly Detection with 2D-3D Alignment
Looking 3D: Anomaly Detection with 2D-3D Alignment
A. Bhunia
Changjian Li
Hakan Bilen
92
4
0
27 Jun 2024
When and How Does In-Distribution Label Help Out-of-Distribution
  Detection?
When and How Does In-Distribution Label Help Out-of-Distribution Detection?
Xuefeng Du
Yiyou Sun
Yixuan Li
75
9
0
28 May 2024
Out-of-distribution Detection in Medical Image Analysis: A survey
Out-of-distribution Detection in Medical Image Analysis: A survey
Zesheng Hong
Yubiao Yue
Yubin Chen
Lele Cong
Huanjie Lin
...
Jialong Xu
Xiaoqi Yang
Hechang Chen
Zhenzhang Li
Sihong Xie
OOD
75
7
0
28 Apr 2024
Toward a Realistic Benchmark for Out-of-Distribution Detection
Toward a Realistic Benchmark for Out-of-Distribution Detection
Pietro Recalcati
Fabio Garcea
Luca Piano
Fabrizio Lamberti
Lia Morra
OODD
151
1
0
16 Apr 2024
Embracing Unknown Step by Step: Towards Reliable Sparse Training in Real
  World
Embracing Unknown Step by Step: Towards Reliable Sparse Training in Real World
Bowen Lei
Dongkuan Xu
Ruqi Zhang
Bani Mallick
UQCV
89
0
0
29 Mar 2024
Anomaly Detection Based on Isolation Mechanisms: A Survey
Anomaly Detection Based on Isolation Mechanisms: A Survey
Yang Cao
Haolong Xiang
Hang Zhang
Ye Zhu
Kai Ming Ting
OOD
79
3
0
16 Mar 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
172
40
0
11 Jan 2024
Understanding normalization in contrastive representation learning and
  out-of-distribution detection
Understanding normalization in contrastive representation learning and out-of-distribution detection
T. L. Gia
Jaehyun Ahn
OODD
80
2
0
23 Dec 2023
Environment-biased Feature Ranking for Novelty Detection Robustness
Stefan Smeu
Elena Burceanu
Emanuela Haller
Andrei Liviu Nicolicioiu
OOD
87
0
0
21 Sep 2023
CA2: Class-Agnostic Adaptive Feature Adaptation for One-class
  Classification
CA2: Class-Agnostic Adaptive Feature Adaptation for One-class Classification
Zilong Zhang
Zhibin Zhao
Deyu Meng
Xingwu Zhang
Xuefeng Chen
VLM
51
1
0
04 Sep 2023
No Free Lunch: The Hazards of Over-Expressive Representations in Anomaly
  Detection
No Free Lunch: The Hazards of Over-Expressive Representations in Anomaly Detection
Tal Reiss
Niv Cohen
Yedid Hoshen
UQCV
102
5
0
12 Jun 2023
SIO: Synthetic In-Distribution Data Benefits Out-of-Distribution
  Detection
SIO: Synthetic In-Distribution Data Benefits Out-of-Distribution Detection
Jingyang Zhang
Nathan Inkawhich
Randolph Linderman
R. Luley
Yiran Chen
H. Li
OODD
71
1
0
25 Mar 2023
Fine-Tuning Deteriorates General Textual Out-of-Distribution Detection
  by Distorting Task-Agnostic Features
Fine-Tuning Deteriorates General Textual Out-of-Distribution Detection by Distorting Task-Agnostic Features
Sishuo Chen
Wenkai Yang
Xiaohan Bi
Xu Sun
OODD
60
15
0
30 Jan 2023
A Call to Reflect on Evaluation Practices for Failure Detection in Image
  Classification
A Call to Reflect on Evaluation Practices for Failure Detection in Image Classification
Paul F. Jaeger
Carsten T. Lüth
Lukas Klein
Till J. Bungert
UQCV
103
38
0
28 Nov 2022
Beyond Mahalanobis-Based Scores for Textual OOD Detection
Beyond Mahalanobis-Based Scores for Textual OOD Detection
Pierre Colombo
Eduardo Dadalto Camara Gomes
Guillaume Staerman
Nathan Noiry
Pablo Piantanida
OODD
102
5
0
24 Nov 2022
Bridging Machine Learning and Sciences: Opportunities and Challenges
Bridging Machine Learning and Sciences: Opportunities and Challenges
Taoli Cheng
UQCVOODAI4CE
63
2
0
24 Oct 2022
OpenOOD: Benchmarking Generalized Out-of-Distribution Detection
OpenOOD: Benchmarking Generalized Out-of-Distribution Detection
Jingkang Yang
Pengyun Wang
Dejian Zou
Zitang Zhou
Kun Ding
...
Kaiyang Zhou
Wayne Zhang
Dan Hendrycks
Yixuan Li
Ziwei Liu
OODD
93
245
0
13 Oct 2022
Fine-grain Inference on Out-of-Distribution Data with Hierarchical
  Classification
Fine-grain Inference on Out-of-Distribution Data with Hierarchical Classification
Randolph Linderman
Jingyang Zhang
Nathan Inkawhich
H. Li
Yiran Chen
OODD
175
7
0
09 Sep 2022
TRUST-LAPSE: An Explainable and Actionable Mistrust Scoring Framework
  for Model Monitoring
TRUST-LAPSE: An Explainable and Actionable Mistrust Scoring Framework for Model Monitoring
Nandita Bhaskhar
D. Rubin
Christopher Lee-Messer
27
5
0
22 Jul 2022
READ: Aggregating Reconstruction Error into Out-of-distribution
  Detection
READ: Aggregating Reconstruction Error into Out-of-distribution Detection
Wenyu Jiang
Yuxin Ge
Hao Cheng
Mingcai Chen
Shuai Feng
Chongjun Wang
OODD
113
12
0
15 Jun 2022
A Unified Model for Multi-class Anomaly Detection
A Unified Model for Multi-class Anomaly Detection
Zhiyuan You
Lei Cui
Yujun Shen
Kai Yang
Xin Lu
Yu Zheng
Xinyi Le
82
230
0
08 Jun 2022
Exposing Outlier Exposure: What Can Be Learned From Few, One, and Zero
  Outlier Images
Exposing Outlier Exposure: What Can Be Learned From Few, One, and Zero Outlier Images
Philipp Liznerski
Lukas Ruff
Robert A. Vandermeulen
Billy Joe Franks
Klaus-Robert Muller
Marius Kloft
UQCV
129
45
0
23 May 2022
Transformaly -- Two (Feature Spaces) Are Better Than One
Transformaly -- Two (Feature Spaces) Are Better Than One
M. Cohen
S. Avidan
ViT
88
30
0
08 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
51
6
0
26 Nov 2021
Improving Novelty Detection using the Reconstructions of Nearest
  Neighbours
Improving Novelty Detection using the Reconstructions of Nearest Neighbours
Michael Mesarcik
E. Ranguelova
A. Boonstra
Rob van Nieuwpoort
46
5
0
11 Nov 2021
OMASGAN: Out-of-Distribution Minimum Anomaly Score GAN for Sample
  Generation on the Boundary
OMASGAN: Out-of-Distribution Minimum Anomaly Score GAN for Sample Generation on the Boundary
Nikolaos Dionelis
Mehrdad Yaghoobi
Sotirios A. Tsaftaris
OODD
47
6
0
28 Oct 2021
Multi-Class Anomaly Detection
Multi-Class Anomaly Detection
Suresh Singh
Minwei Luo
Yu Li
48
1
0
28 Oct 2021
A Unified Survey on Anomaly, Novelty, Open-Set, and Out-of-Distribution
  Detection: Solutions and Future Challenges
A Unified Survey on Anomaly, Novelty, Open-Set, and Out-of-Distribution Detection: Solutions and Future Challenges
Mohammadreza Salehi
Hossein Mirzaei
Dan Hendrycks
Yixuan Li
M. Rohban
Mohammad Sabokrou
OOD
162
199
0
26 Oct 2021
Generalized Out-of-Distribution Detection: A Survey
Generalized Out-of-Distribution Detection: A Survey
Jingkang Yang
Kaiyang Zhou
Yixuan Li
Ziwei Liu
309
952
0
21 Oct 2021
Types of Out-of-Distribution Texts and How to Detect Them
Types of Out-of-Distribution Texts and How to Detect Them
Udit Arora
William Huang
He He
OODD
279
101
0
14 Sep 2021
No True State-of-the-Art? OOD Detection Methods are Inconsistent across
  Datasets
No True State-of-the-Art? OOD Detection Methods are Inconsistent across Datasets
Fahim Tajwar
Ananya Kumar
Sang Michael Xie
Percy Liang
OODD
70
25
0
12 Sep 2021
On the Out-of-distribution Generalization of Probabilistic Image
  Modelling
On the Out-of-distribution Generalization of Probabilistic Image Modelling
Mingtian Zhang
Andi Zhang
Jingyu Sun
OODD
146
45
0
04 Sep 2021
CODEs: Chamfer Out-of-Distribution Examples against Overconfidence Issue
CODEs: Chamfer Out-of-Distribution Examples against Overconfidence Issue
Keke Tang
Dingruibo Miao
Weilong Peng
Jianpeng Wu
Yawen Shi
Zhaoquan Gu
Zhihong Tian
Wenping Wang
OODD
203
31
0
13 Aug 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
104
19
0
09 Aug 2021
Mixture Outlier Exposure: Towards Out-of-Distribution Detection in
  Fine-grained Environments
Mixture Outlier Exposure: Towards Out-of-Distribution Detection in Fine-grained Environments
Jingyang Zhang
Nathan Inkawhich
Randolph Linderman
Yiran Chen
H. Li
OODD
92
58
0
07 Jun 2021
Mean-Shifted Contrastive Loss for Anomaly Detection
Mean-Shifted Contrastive Loss for Anomaly Detection
Tal Reiss
Yedid Hoshen
81
118
0
07 Jun 2021
Comparison of Anomaly Detectors: Context Matters
Comparison of Anomaly Detectors: Context Matters
V. Škvára
Jan Francå
Matěj Zorek
Tomás Pevný
Václav Smídl
62
9
0
11 Dec 2020
Unsupervised Anomaly Detection From Semantic Similarity Scores
Nima Rafiee
Rahil Gholamipoor
M. Kollmann
OODD
34
2
0
01 Dec 2020
Deep Anomaly Detection by Residual Adaptation
Deep Anomaly Detection by Residual Adaptation
Lucas Deecke
Lukas Ruff
Robert A. Vandermeulen
Hakan Bilen
UQCV
84
4
0
05 Oct 2020
A Unifying Review of Deep and Shallow Anomaly Detection
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
148
806
0
24 Sep 2020
On the Nature and Types of Anomalies: A Review of Deviations in Data
On the Nature and Types of Anomalies: A Review of Deviations in Data
Ralph Foorthuis
115
89
0
30 Jul 2020
A General Framework For Detecting Anomalous Inputs to DNN Classifiers
A General Framework For Detecting Anomalous Inputs to DNN Classifiers
Jayaram Raghuram
Varun Chandrasekaran
S. Jha
Suman Banerjee
AAML
106
35
0
29 Jul 2020
A Benchmark of Medical Out of Distribution Detection
A Benchmark of Medical Out of Distribution Detection
Tianshi Cao
Chinwei Huang
D. Y. Hui
Joseph Paul Cohen
OOD
85
59
0
08 Jul 2020
Modeling the Distribution of Normal Data in Pre-Trained Deep Features
  for Anomaly Detection
Modeling the Distribution of Normal Data in Pre-Trained Deep Features for Anomaly Detection
Oliver Rippel
Patrick Mertens
Dorit Merhof
160
241
0
28 May 2020
Deep Nearest Neighbor Anomaly Detection
Deep Nearest Neighbor Anomaly Detection
Liron Bergman
Niv Cohen
Yedid Hoshen
UQCV
111
161
0
24 Feb 2020
Bayesian Variational Autoencoders for Unsupervised Out-of-Distribution
  Detection
Bayesian Variational Autoencoders for Unsupervised Out-of-Distribution Detection
Erik A. Daxberger
José Miguel Hernández-Lobato
UQCV
103
63
0
11 Dec 2019
High- and Low-level image component decomposition using VAEs for
  improved reconstruction and anomaly detection
High- and Low-level image component decomposition using VAEs for improved reconstruction and anomaly detection
David Zimmerer
Jens Petersen
Klaus Maier-Hein
DRL
42
7
0
27 Nov 2019
Natural Adversarial Examples
Natural Adversarial Examples
Dan Hendrycks
Kevin Zhao
Steven Basart
Jacob Steinhardt
Basel Alomair
OODD
296
1,486
0
16 Jul 2019
1