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...
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IEEE
2025-01-01
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| Online Access: | https://ieeexplore.ieee.org/document/11025478/ |
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| author | Muhammad Asad Ihsan Ullah Muhammad Adeel Hafeez Ganesh Sistu Michael G. Madden |
| author_facet | Muhammad Asad Ihsan Ullah Muhammad Adeel Hafeez Ganesh Sistu Michael G. Madden |
| author_sort | Muhammad Asad |
| collection | DOAJ |
| description | 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 present is to use an adversarial network generator to generate an anomaly score for inputs using the reconstruction loss. However, because this technique uses a competitive training process, it can be unreliable, with its performance being inconsistent during each adversarial training step. This inconsistency arises from changes in the network’s ability to detect anomalies. In this paper, we propose a revised framework for generative probabilistic novelty detection. We use a similar adversarial autoencoder-based framework but with a lightweight deep network, a novel training paradigm, and a probabilistic score to compute the reconstruction loss. Our methodology calculates the probability of whether a sample comes from the inlier distribution or not. The proposed approach can be applied to anomaly and outlier detection in images and videos. We present the results on multiple benchmark datasets, including the challenging UCSD Ped2 dataset for video anomaly detection. Our results illustrate that our proposed method learns the inlier classes and differentiates them from the outlier classes effectively, leading to better results than the baseline and state-of-the-art methods in several benchmark datasets. |
| format | Article |
| id | doaj-art-640fe831f6f5414da5d831a38bb6ccfb |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
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| series | IEEE Access |
| spelling | doaj-art-640fe831f6f5414da5d831a38bb6ccfb2025-08-20T03:45:30ZengIEEEIEEE Access2169-35362025-01-0113985309854110.1109/ACCESS.2025.357708011025478A Probabilistic Adversarial Autoencoder for Novelty Detection: Leveraging Lightweight Design and Reconstruction LossMuhammad Asad0https://orcid.org/0000-0002-6398-0320Ihsan Ullah1https://orcid.org/0000-0002-7964-5199Muhammad Adeel Hafeez2https://orcid.org/0000-0002-3593-7448Ganesh Sistu3https://orcid.org/0009-0003-1683-9257Michael G. Madden4https://orcid.org/0000-0002-4443-7285Machine Learning Research Group, School of Computer Science, University of Galway, Galway, IrelandMachine Learning Research Group, School of Computer Science, University of Galway, Galway, IrelandMachine Learning Research Group, School of Computer Science, University of Galway, Galway, IrelandMachine Learning Research Group, School of Computer Science, University of Galway, Galway, IrelandMachine Learning Research Group, School of Computer Science, University of Galway, Galway, IrelandA 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 present is to use an adversarial network generator to generate an anomaly score for inputs using the reconstruction loss. However, because this technique uses a competitive training process, it can be unreliable, with its performance being inconsistent during each adversarial training step. This inconsistency arises from changes in the network’s ability to detect anomalies. In this paper, we propose a revised framework for generative probabilistic novelty detection. We use a similar adversarial autoencoder-based framework but with a lightweight deep network, a novel training paradigm, and a probabilistic score to compute the reconstruction loss. Our methodology calculates the probability of whether a sample comes from the inlier distribution or not. The proposed approach can be applied to anomaly and outlier detection in images and videos. We present the results on multiple benchmark datasets, including the challenging UCSD Ped2 dataset for video anomaly detection. Our results illustrate that our proposed method learns the inlier classes and differentiates them from the outlier classes effectively, leading to better results than the baseline and state-of-the-art methods in several benchmark datasets.https://ieeexplore.ieee.org/document/11025478/Anomaly detectionadversarial autoencodersreconstruction losslatent spaceprobability distributiongenerative adversarial networks (GANs) |
| spellingShingle | Muhammad Asad Ihsan Ullah Muhammad Adeel Hafeez Ganesh Sistu Michael G. Madden A Probabilistic Adversarial Autoencoder for Novelty Detection: Leveraging Lightweight Design and Reconstruction Loss IEEE Access Anomaly detection adversarial autoencoders reconstruction loss latent space probability distribution generative adversarial networks (GANs) |
| title | A Probabilistic Adversarial Autoencoder for Novelty Detection: Leveraging Lightweight Design and Reconstruction Loss |
| title_full | A Probabilistic Adversarial Autoencoder for Novelty Detection: Leveraging Lightweight Design and Reconstruction Loss |
| title_fullStr | A Probabilistic Adversarial Autoencoder for Novelty Detection: Leveraging Lightweight Design and Reconstruction Loss |
| title_full_unstemmed | A Probabilistic Adversarial Autoencoder for Novelty Detection: Leveraging Lightweight Design and Reconstruction Loss |
| title_short | A Probabilistic Adversarial Autoencoder for Novelty Detection: Leveraging Lightweight Design and Reconstruction Loss |
| title_sort | probabilistic adversarial autoencoder for novelty detection leveraging lightweight design and reconstruction loss |
| topic | Anomaly detection adversarial autoencoders reconstruction loss latent space probability distribution generative adversarial networks (GANs) |
| url | https://ieeexplore.ieee.org/document/11025478/ |
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