Abstract
Industrial data is often available only in an unlabeled form as obtaining the label (the response) for the input data can be a challenging and time-consuming task. This Quality Quandaries provides an overview of active learning-based sampling methods for streamlining the development of classification and regression models in label-scarce environments. A case study on active learning for vision-based industrial inspection is presented. The case study shows how selecting the most informative data points to label can at a fraction of the cost achieve model performances similar to the case where all input data is labeled.
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