Unveiling the Anomalies in an Ever-Changing World: A Benchmark for Pixel-Level Anomaly Detection in Continual Learning
<|reference_start|>Unveiling the Anomalies in an Ever-Changing World: A Benchmark for Pixel-Level Anomaly Detection in Continual Learning: Anomaly Detection is a relevant problem in numerous real-world applications, especially when dealing with images. However, little attention has been paid to the issue of changes over time in the input data distribution, which may cause a significant decrease in performance. In this study, we investigate the problem of Pixel-Level Anomaly Detection in the Continual Learning setting, where new data arrives over time and the goal is to perform well on new and old data. We implement several state-of-the-art techniques to solve the Anomaly Detection problem in the classic setting and adapt them to work in the Continual Learning setting. To validate the approaches, we use a real-world dataset of images with pixel-based anomalies to provide a reliable benchmark and serve as a foundation for further advancements in the field. We provide a comprehensive analysis, discussing which Anomaly Detection methods and which families of approaches seem more suitable for the Continual Learning setting.<|reference_end|>
@article{bugarin2024unveiling,
    title={Unveiling the Anomalies in an Ever-Changing World: A Benchmark for
  Pixel-Level Anomaly Detection in Continual Learning},
    author={Nikola Bugarin, Jovana Bugaric, Manuel Barusco, Davide Dalle Pezze,
  Gian Antonio Susto},
    journal={arXiv preprint arXiv:2403.15463},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.15463},
    primaryClass={cs.CV cs.LG}
}