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Found 5 papers, 2 papers with code
Real-Time Adaptive Safety-Critical Control with Gaussian Processes in High-Order Uncertain Models
Subsequently, the hyperparameters of the Gaussian model are trained with a specially compound kernel, and the Gaussian model's online inferential capability and computational efficiency are strengthened by updating a solitary inducing point derived from new samples, in conjunction with the learned hyperparameters.
Online Efficient Safety-Critical Control for Mobile Robots in Unknown Dynamic Multi-Obstacle Environments
This paper proposes a LiDAR-based goal-seeking and exploration framework, addressing the efficiency of online obstacle avoidance in unstructured environments populated with static and moving obstacles.
Unsupervised Image Anomaly Detection and Segmentation Based on Pre-trained Feature Mapping
This paper proposes a novel framework, named as Pre-trained Feature Mapping (PFM), for unsupervised image anomaly detection and segmentation.
Ranked #51 on
Anomaly Detection
on MVTec AD
Industrial Image Anomaly Localization Based on Gaussian Clustering of Pretrained Feature
Anomaly localization is valuable for improvement of complex production processing in smart manufacturing system.
Ranked #70 on
Anomaly Detection
on MVTec AD
Attention Based Joint Learning for Supervised Premature Ventricular Contraction Differentiation with Unsupervised Abnormal Beat Segmentation
Though the training data is only labeled to supervise theclassifier, the segmenter and the classifier are trained in an end-to-end manner sothat optimizing classification performance also adjusts how the abnormal beats aresegmented.
