Active Learning Loop
An iterative process where a model learns from strategically chosen data and improves itself
Overview
Active Learning Loops refer to the iterative process of refining an AI model in active learning, where the model strategically selects the most informative data points for human annotation. The newly labeled data is then used to retrain the model, further improving its performance and selecting future data points, thus continuing the loop of refinement.
Iterative Data Selection and Learning
The active learning loop is a repetitive process where a model selects informative data, and then learns from it.
Self-Improving Cycle
The model chooses the data, learns from labeled data, then further improving its capabilities, creating a self-improving loop.
Continuous Model Improvement
The goal is for the model to improve over time by learning from its mistakes and continually improving its selection of informative data.