All publications

Deep anomaly detection on set data: Survey and comparison

  • DOI: 10.1016/j.patcog.2024.110381
  • Link: https://doi.org/10.1016/j.patcog.2024.110381
  • Department: Department of Computer Science, Artificial Intelligence Center
  • Annotation:
    Detecting anomalous samples in set data is a problem attracting increased interest due to novel modalities, such as point-cloud data produced by lidars. Novel methods including those based on deep neural networks are often tuned for a single purpose prohibiting intuition of how relevant they are for another purpose or application domains. The aim of this survey is to: (i) review elementary concepts of anomaly detection of set data, (ii) identify the building blocks of deep anomaly detectors, and (iii) analyze the impact of these blocks on performance. The impact is studied in a large experimental comparison on a variety of benchmark datasets. The results reveal that the main factor determining the performance is the type of anomalies in the dataset. While deep methods embedding the whole set to a single fixed vector perform well on point cloud data, the methods embedding each feature vector independently are better for datasets from multi-instance learning. Moreover, sophisticated methods utilizing transformer blocks are frequently inferior to simple models with properly optimized hyperparameters. An independent factor in performance is the cardinality of sets, the proper treatment of which remains an open problem, as the existing analytical solution was found to be inaccurate.

Comparison of Anomaly Detectors: Context Matters

  • DOI: 10.1109/TNNLS.2021.3116269
  • Link: https://doi.org/10.1109/TNNLS.2021.3116269
  • Department: Department of Computer Science, Artificial Intelligence Center
  • Annotation:
    Deep generative models are challenging the classical methods in the field of anomaly detection nowadays. Every newly published method provides evidence of outperforming its predecessors, sometimes with contradictory results. The objective of this article is twofold: to compare anomaly detection methods of various paradigms with a focus on deep generative models and identification of sources of variability that can yield different results. The methods were compared on popular tabular and image datasets. We identified that the main sources of variability are the experimental conditions: 1) the type of dataset (tabular or image) and the nature of anomalies (statistical or semantic) and 2) strategy of selection of hyperparameters, especially the number of available anomalies in the validation set. Methods perform differently in different contexts, i.e., under a different combination of experimental conditions together with computational time. This explains the variability of the previous results and highlights the importance of careful specification of the context in the publication of a new method. All our code and results are available for download.

Semi-supervised deep networks for plasma state identification

  • DOI: 10.1088/1361-6587/ac9926
  • Link: https://doi.org/10.1088/1361-6587/ac9926
  • Department: Department of Computer Science, Artificial Intelligence Center
  • Annotation:
    Correct and timely detection of plasma confinement regimes and edge localized modes (ELMs) is important for improving the operation of tokamaks. Existing machine learning approaches detect these regimes as a form of post-processing of experimental data. Moreover, they are typically trained on a large dataset of tens of labeled discharges, which may be costly to build. We investigate the ability of current machine learning approaches to detect the confinement regime and ELMs with the smallest possible delay after the latest measurement. We also demonstrate that including unlabeled data into the training process can improve the results in a situation where only a limited set of reliable labels is available. All training and validation is performed on data from the COMPASS tokamak. The InceptionTime architecture trained using a semi-supervised approach was found to be the most accurate method based on the set of tested variants. It is able to achieve good overall accuracy of the regime classification at the time instant of 100 μs delayed behind the latest data record. We also evaluate the capability of the model to correctly predict class transitions. While ELM occurrence can be detected with a tolerance smaller than 50 μs, detection of the confinement regime transition is more demanding and it was successful with 2 ms tolerance. Sensitivity studies to different values of model parameters are provided. We believe that the achieved accuracy is acceptable in practice and the method could be used in real-time operation.

Responsible person Ing. Mgr. Radovan Suk