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Found 4 papers, 3 papers with code
Stream-based Active Learning with Verification Latency in Non-stationary Environments
Furthermore, we propose a drift-dependent dynamic budget strategy, which uses a variable distribution of the labelling budget over time, after a detected drift.
Task-Sensitive Concept Drift Detector with Constraint Embedding
In the proposed framework, the actual method to detect a change in the statistics of incoming data samples can be chosen freely.
Estimating the electrical power output of industrial devices with end-to-end time-series classification in the presence of label noise
However, sensor failures result in mislabeled training data samples which are hard to detect and remove from the dataset.
Real-World Anomaly Detection by using Digital Twin Systems and Weakly-Supervised Learning
The approaches make use of a Digital Twin to generate a training dataset which simulates the normal operation of the machinery, along with a small set of labeled anomalous measurement from the real machinery.
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