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Found 7 papers, 4 papers with code
Learning Multi-Pattern Normalities in the Frequency Domain for Efficient Time Series Anomaly Detection
Anomaly detection significantly enhances the robustness of cloud systems.
LARA: A Light and Anti-overfitting Retraining Approach for Unsupervised Time Series Anomaly Detection
Retraining a large neural network model with limited data is vulnerable to overfitting.
Resisting Backdoor Attacks in Federated Learning via Bidirectional Elections and Individual Perspective
Existing approaches defend against backdoor attacks in federated learning (FL) mainly through a) mitigating the impact of infected models, or b) excluding infected models.
PePNet: A Periodicity-Perceived Workload Prediction Network Supporting Rare Occurrence of Heavy Workload
This impairs the prediction accuracy of neural network-based models on heavy workload.
LCE-Calib: Automatic LiDAR-Frame/Event Camera Extrinsic Calibration With A Globally Optimal Solution
This paper proposes an automatic checkerboard-based approach to calibrate extrinsics between a LiDAR and a frame/event camera, where four contributions are presented.
MMFN: Multi-Modal-Fusion-Net for End-to-End Driving
Inspired by the fact that humans use diverse sensory organs to perceive the world, sensors with different modalities are deployed in end-to-end driving to obtain the global context of the 3D scene.
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