A Probabilistic Adversarial Autoencoder for Novelty Detection: Leveraging Lightweight Design and Reconstruction Loss

A Probabilistic Adversarial Autoencoder for Novelty Detection: Leveraging Lightweight Design and Reconstruction Loss

A novelty detection task involves identifying whether a data point is an outlier, given a training dataset that primarily captures the distribution of inliers. The novel class is usually absent, poorly sampled, or not well defined in the training data. A common technique for anomaly detection at pre...

Full description

Saved in:
Bibliographic Details
Main Authors: Muhammad Asad, Ihsan Ullah, Muhammad Adeel Hafeez, Ganesh Sistu, Michael G. Madden
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Access
Subjects:
Anomaly detection
adversarial autoencoders
reconstruction loss
latent space
probability distribution
generative adversarial networks (GANs)
Online Access:https://ieeexplore.ieee.org/document/11025478/
Tags: Add Tag
No Tags, Be the first to tag this record!

Internet

https://ieeexplore.ieee.org/document/11025478/

Similar Items