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Semi-supervised detection of structural damage using Variational
  Autoencoder and a One-Class Support Vector Machine

Semi-supervised detection of structural damage using Variational Autoencoder and a One-Class Support Vector Machine

11 October 2022
A. Pollastro
Giusiana Testa
A. Bilotta
R. Prevete
    DRL
ArXivPDFHTML

Papers citing "Semi-supervised detection of structural damage using Variational Autoencoder and a One-Class Support Vector Machine"

2 / 2 papers shown
Title
SincVAE: a New Approach to Improve Anomaly Detection on EEG Data Using
  SincNet and Variational Autoencoder
SincVAE: a New Approach to Improve Anomaly Detection on EEG Data Using SincNet and Variational Autoencoder
A. Pollastro
Francesco Isgrò
R. Prevete
42
2
0
25 Jun 2024
Closing the sim-to-real gap in guided wave damage detection with
  adversarial training of variational auto-encoders
Closing the sim-to-real gap in guided wave damage detection with adversarial training of variational auto-encoders
Ishan D. Khurjekar
J. Harley
AAML
24
10
0
26 Jan 2022
1