Advanced methods in data labeling
Active learning and semi-automated learning are popular machine learning techniques that help overcome the challenge of data labeling. Both involve presenting uncertain or challenging labels to human annotators for feedback; the key difference lies in the overall strategy and decision-making process. Let’s break down the distinction.
Active learning
Active learning is a machine learning paradigm in which a model is trained on a subset of the data, and then the model actively selects the most informative examples for labeling to improve its performance. The following list discusses various features of this method:
- Workflow: The initial model is trained on a small labeled dataset. The model identifies instances where it is uncertain or likely to make errors. These uncertain or challenging instances are presented to human annotators for labeling. The model is updated with the new labeled data, and the process iterates.
- Benefits...
