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Anomaly Detection: How to Artificially Increase your F1-Score with a
  Biased Evaluation Protocol

Anomaly Detection: How to Artificially Increase your F1-Score with a Biased Evaluation Protocol

30 June 2021
Damien Fourure
Muhammad Usama Javaid
N. Posocco
Simon Tihon
ArXivPDFHTML

Papers citing "Anomaly Detection: How to Artificially Increase your F1-Score with a Biased Evaluation Protocol"

10 / 10 papers shown
Title
Has the Deep Neural Network learned the Stochastic Process? An Evaluation Viewpoint
Has the Deep Neural Network learned the Stochastic Process? An Evaluation Viewpoint
H. Kumar
Beomseok Kang
Biswadeep Chakraborty
Saibal Mukhopadhyay
51
0
0
28 Jan 2025
Weakly-Supervised Anomaly Detection in Surveillance Videos Based on
  Two-Stream I3D Convolution Network
Weakly-Supervised Anomaly Detection in Surveillance Videos Based on Two-Stream I3D Convolution Network
Sareh Nejad
Anwar Haque
21
1
0
13 Nov 2024
FedAD-Bench: A Unified Benchmark for Federated Unsupervised Anomaly
  Detection in Tabular Data
FedAD-Bench: A Unified Benchmark for Federated Unsupervised Anomaly Detection in Tabular Data
Ahmed Anwar
Brian B. Moser
Dayananda Herurkar
Federico Raue
Vinit Hegiste
T. Legler
Andreas Dengel
FedML
37
0
0
08 Aug 2024
Can Tree Based Approaches Surpass Deep Learning in Anomaly Detection? A
  Benchmarking Study
Can Tree Based Approaches Surpass Deep Learning in Anomaly Detection? A Benchmarking Study
Santonu Sarkar
Shanay Mehta
Nicole Fernandes
Jyotirmoy Sarkar
Snehanshu Saha
41
2
0
11 Feb 2024
Unsupervised Anomaly Detection for Auditing Data and Impact of
  Categorical Encodings
Unsupervised Anomaly Detection for Auditing Data and Impact of Categorical Encodings
Ajay Chawda
S. Grimm
Marius Kloft
35
4
0
25 Oct 2022
Estimating the Contamination Factor's Distribution in Unsupervised
  Anomaly Detection
Estimating the Contamination Factor's Distribution in Unsupervised Anomaly Detection
Lorenzo Perini
Paul-Christian Buerkner
Arto Klami
23
14
0
19 Oct 2022
Automatic Tuberculosis and COVID-19 cough classification using deep
  learning
Automatic Tuberculosis and COVID-19 cough classification using deep learning
Madhurananda Pahar
Marisa Klopper
B. Reeve
Robin Warren
G. Theron
A. Diacon
T. Niesler
36
16
0
11 May 2022
A Revealing Large-Scale Evaluation of Unsupervised Anomaly Detection
  Algorithms
A Revealing Large-Scale Evaluation of Unsupervised Anomaly Detection Algorithms
Maxime Alvarez
Jean-Charles Verdier
D'Jeff K. Nkashama
Marc Frappier
Pierre Martin Tardif
F. Kabanza
23
17
0
21 Apr 2022
Deep-Disaster: Unsupervised Disaster Detection and Localization Using
  Visual Data
Deep-Disaster: Unsupervised Disaster Detection and Localization Using Visual Data
Soroor Shekarizadeh
R. Rastgoo
Saif M. Al-Kuwari
Mohammad Sabokrou
27
2
0
31 Jan 2022
Boosting Anomaly Detection Using Unsupervised Diverse Test-Time
  Augmentation
Boosting Anomaly Detection Using Unsupervised Diverse Test-Time Augmentation
Seffi Cohen
Niv Goldshlager
L. Rokach
Bracha Shapira
18
9
0
29 Oct 2021
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