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Found 8 papers, 1 papers with code
Multi-source Few-shot Domain Adaptation
Multi-source Domain Adaptation (MDA) aims to transfer predictive models from multiple, fully-labeled source domains to an unlabeled target domain.
Scene-aware Learning Network for Radar Object Detection
In this paper, we propose a scene-aware radar learning framework for accurate and robust object detection.
Prototypical Cross-domain Self-supervised Learning for Few-shot Unsupervised Domain Adaptation
In this paper, we propose an end-to-end Prototypical Cross-domain Self-Supervised Learning (PCS) framework for Few-shot Unsupervised Domain Adaptation (FUDA).
Ranked #6 on
Semantic Segmentation
on DensePASS
Using Ensemble Classifiers to Detect Incipient Anomalies
Incipient anomalies present milder symptoms compared to severe ones, and are more difficult to detect and diagnose due to their close resemblance to normal operating conditions.
Generalizing Fault Detection Against Domain Shifts Using Stratification-Aware Cross-Validation
Incipient anomalies present milder symptoms compared to severe ones, and are more difficult to detect and diagnose due to their close resemblance to normal operating conditions.
Exploiting Uncertainties from Ensemble Learners to Improve Decision-Making in Healthcare AI
Ensemble learning is widely applied in Machine Learning (ML) to improve model performance and to mitigate decision risks.
Are Ensemble Classifiers Powerful Enough for the Detection and Diagnosis of Intermediate-Severity Faults?
Intermediate-Severity (IS) faults present milder symptoms compared to severe faults, and are more difficult to detect and diagnose due to their close resemblance to normal operating conditions.
An Encoder-Decoder Based Approach for Anomaly Detection with Application in Additive Manufacturing
We show that the encoder-decoder model is able to identify the injected anomalies in a modern manufacturing process in an unsupervised fashion.
