Detecting and Categorizing Anomalous Behavior in Bio-Regenerative Life Support System Telemetry
Unsupervised anomaly detection methods MDI and DAMP are used to identify and categorize different types of anomalies in telemetry data from the EDEN ISS space greenhouse, providing insights into systematic issues that could impact food production and operational efficiency.
自律走行における多様なセンサデータを用いた異常検知のための大規模ベンチマーク「AnoVox」
AnoVoxは、自律走行における異常検知のための大規模で多様なベンチマークデータセットを提供する。センサデータ、時間的な異常、空間的な異常を含む、現実に即した異常検知課題に取り組むことができる。
Adapting Anomaly Detection Models to Operational Data Changes: Evaluating Blind and Informed Retraining Techniques
Periodic model retraining can significantly improve the performance of anomaly detection models over time, but the choice of retraining technique (blind vs informed) and data (full-history vs sliding window) depends on the specific anomaly detection model.
AD-NEv++: A Scalable Multi-Architecture Neuroevolution Framework for Multivariate Anomaly Detection
AD-NEv++ is a neuroevolution-based framework that synergically combines subspace evolution, model evolution, and fine-tuning to optimize autoencoder architectures, including graph-based models, for multivariate anomaly detection. The framework supports a wide spectrum of neural layers, including attention, dense, and graph convolutional layers, and outperforms well-known deep learning architectures and neuroevolution-based approaches on benchmark datasets.
MambaAD: A Mamba-based Approach for Efficient and Effective Multi-class Unsupervised Anomaly Detection
MambaAD, a novel framework that leverages the Mamba architecture, achieves state-of-the-art performance in multi-class unsupervised anomaly detection tasks while maintaining low model complexity.
Lifelong Continual Learning for Anomaly Detection: Challenges, Perspectives, and Insights
Lifelong learning can provide significant advantages for anomaly detection models by enabling simultaneous adaptation and knowledge retention, leading to more robust and comprehensive models that can effectively handle dynamic environments.
Long-Tailed Anomaly Detection with Learnable Class Names for Scalable and Robust Defect Identification
LTAD combines anomaly detection by reconstruction and semantic anomaly detection to detect defects across multiple and long-tailed image classes, without relying on dataset class names. It learns pseudo-class names and uses a VAE-based data augmentation to address the long-tailed distribution of real-world applications.
RealNet: Anomaly Detection Framework with Synthetic Anomaly Generation
RealNet introduces innovative anomaly detection methods with synthetic anomaly generation, feature selection, and reconstruction residuals.
Dual-path Frequency Discriminators for Few-shot Anomaly Detection in Industrial Images
주파수 관점에서 적은 샷 이상치 감지를 위한 이중 경로 주파수 판별기의 제안
Exploring Multi-class Anomaly Detection with MINT-AD Model
Introducing the MINT-AD model for multi-class anomaly detection, leveraging class-aware query embeddings to mitigate inter-class interference.
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