参考资料 [1] An, Jinwon, and Sungzoon Cho. "Variational autoencoder based anomaly detection using reconstruction probability." Special Lecture on IE 2.1 (2015).
Anomaly detectionDefect inspectionAutoencodersMachine visionIn this paper, the unsupervised autoencoder learning for automated defect detection in manufacturing is evaluated, where only the defect-free samples are required for the model training. The loss function of a Convolutional Autoencoder (CAE) ...
We propose an anomaly detection method using the reconstruction probability from the variational autoencoder. The reconstruction probability is a probabilistic measure that takes into account the variability of the distribution of variables. The reconstruction probability has a theoretical background making it...
Exploiting Autoencoder-Based Anomaly Detection to Enhance Cybersecurity in Power Grids The evolution of smart grids has led to technological advances and a demand for more efficient and sustainable energy systems. However, the deployment of communication systems in smart grids has increased the threat ...
Data quality significantly impacts the results of data analytics. Researchers have proposed machine learning based anomaly detection techniques to identify incorrect data. Existing approaches fail to (1) identify the underlying domain constraints violated by the anomalous data, and (2) generate explanations...
Anomaly detection aims to discover patterns in data that do not conform to the expected normal behaviour. One of the significant issues for anomaly detection techniques is the availability of labeled data for training/validation of models. In this paper, we proposed a hyper approach based on Long...
Autoencoders (AE) can be used as an unsupervised computer network anomaly detector for cyber security use cases. Anomaly detection is typically achieved by comparing the resulting AE Reconstruction Error (RE) value for a given data item against a threshold value, with values below threshold belongi...
This model is used for tasks such as anomaly detection, data compression, and sequence regeneration. In particular, the LSTM autoencoder has excellent performance in various applications, as it can effectively extract and restore important features of time series data. For example, in the anomaly ...
Anomaly detection is a significant task in sensors’ signal processing since interpreting an abnormal signal can lead to making a high-risk decision in terms of sensors’ applications. Deep learning algorithms are effective tools for anomaly detection due to their capability to address imbalanced datase...
Learning Spatiotemporal Representation Based on 3D Autoencoder for Anomaly DetectionBecause of ambiguous definition of anomaly and the complexity of real data, anomaly detection in videos is of utmost importance in intelligent video surveillance. We approach this problem by learning......