Enhancing Anomaly Detection Through Latent Space Manipulation in Autoencoders: A Comparative Analysis
This article explores the practical implementation of autoencoders for anomaly detection, emphasizing their latent space manipulation and applicability across various domains. This study highlights the impact of optimizing parameter configurations, lightweight architectures, and training methodologi...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-12-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/15/1/286 |
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| author | Tomasz Walczyna Damian Jankowski Zbigniew Piotrowski |
| author_facet | Tomasz Walczyna Damian Jankowski Zbigniew Piotrowski |
| author_sort | Tomasz Walczyna |
| collection | DOAJ |
| description | This article explores the practical implementation of autoencoders for anomaly detection, emphasizing their latent space manipulation and applicability across various domains. This study highlights the impact of optimizing parameter configurations, lightweight architectures, and training methodologies to enhance anomaly detection performance. A comparative analysis of autoencoders, Variational Autoencoders, and their modified counterparts was conducted within a tailored experimental environment designed to simulate real-world scenarios. The results demonstrate that these models, when fine-tuned, achieve significant improvements in detection accuracy, specificity, and sensitivity while maintaining computational efficiency. The findings underscore the importance of lightweight, practical models and the integration of streamlined training processes in developing effective anomaly detection systems. This study provides valuable insights into advancing machine learning methods for real-world applications and sets the stage for further refinement of autoencoder-based approaches. |
| format | Article |
| id | doaj-art-3ce5de355e8d44c6ad6a5c4642788e48 |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-3ce5de355e8d44c6ad6a5c4642788e482025-01-10T13:15:02ZengMDPI AGApplied Sciences2076-34172024-12-0115128610.3390/app15010286Enhancing Anomaly Detection Through Latent Space Manipulation in Autoencoders: A Comparative AnalysisTomasz Walczyna0Damian Jankowski1Zbigniew Piotrowski2Faculty of Electronics, Military University of Technology, Electronics and Telecommunications, 00-908 Warszawa, PolandFaculty of Electronics, Military University of Technology, Electronics and Telecommunications, 00-908 Warszawa, PolandFaculty of Electronics, Military University of Technology, Electronics and Telecommunications, 00-908 Warszawa, PolandThis article explores the practical implementation of autoencoders for anomaly detection, emphasizing their latent space manipulation and applicability across various domains. This study highlights the impact of optimizing parameter configurations, lightweight architectures, and training methodologies to enhance anomaly detection performance. A comparative analysis of autoencoders, Variational Autoencoders, and their modified counterparts was conducted within a tailored experimental environment designed to simulate real-world scenarios. The results demonstrate that these models, when fine-tuned, achieve significant improvements in detection accuracy, specificity, and sensitivity while maintaining computational efficiency. The findings underscore the importance of lightweight, practical models and the integration of streamlined training processes in developing effective anomaly detection systems. This study provides valuable insights into advancing machine learning methods for real-world applications and sets the stage for further refinement of autoencoder-based approaches.https://www.mdpi.com/2076-3417/15/1/286anomaliesdetection of anomaliesneural networksdeep learningmachine learningautoencoders |
| spellingShingle | Tomasz Walczyna Damian Jankowski Zbigniew Piotrowski Enhancing Anomaly Detection Through Latent Space Manipulation in Autoencoders: A Comparative Analysis Applied Sciences anomalies detection of anomalies neural networks deep learning machine learning autoencoders |
| title | Enhancing Anomaly Detection Through Latent Space Manipulation in Autoencoders: A Comparative Analysis |
| title_full | Enhancing Anomaly Detection Through Latent Space Manipulation in Autoencoders: A Comparative Analysis |
| title_fullStr | Enhancing Anomaly Detection Through Latent Space Manipulation in Autoencoders: A Comparative Analysis |
| title_full_unstemmed | Enhancing Anomaly Detection Through Latent Space Manipulation in Autoencoders: A Comparative Analysis |
| title_short | Enhancing Anomaly Detection Through Latent Space Manipulation in Autoencoders: A Comparative Analysis |
| title_sort | enhancing anomaly detection through latent space manipulation in autoencoders a comparative analysis |
| topic | anomalies detection of anomalies neural networks deep learning machine learning autoencoders |
| url | https://www.mdpi.com/2076-3417/15/1/286 |
| work_keys_str_mv | AT tomaszwalczyna enhancinganomalydetectionthroughlatentspacemanipulationinautoencodersacomparativeanalysis AT damianjankowski enhancinganomalydetectionthroughlatentspacemanipulationinautoencodersacomparativeanalysis AT zbigniewpiotrowski enhancinganomalydetectionthroughlatentspacemanipulationinautoencodersacomparativeanalysis |