Combine Weak Supervision and Active learning

Combining weak supervision and active learning can be a powerful way to build datasets with limited labeled data. Here's a general process for doing this:

Generate initial weak supervision signals: Use a variety of methods such as rule-based systems, pattern-matching algorithms, or other automated techniques to generate weak supervision signals for your dataset. These signals can be used to create initial labels for the data.

Train a model using the weak supervision signals: Use the initial labels generated from the weak supervision signals to train a model on your dataset.

Use active learning to identify samples to label: Once you have a trained model, use active learning techniques to identify samples that are most likely to improve the model's performance if labeled. For example, you can use uncertainty sampling to identify samples that the model is uncertain about.

Label the selected samples: Label the selected samples using human annotators or other labeling techniques.

Update the model and repeat: After labeling the selected samples, update the model using the new labels and repeat the active learning process to identify additional samples to label. Continue this process until you have labeled enough samples to train a high-performing model.

By combining weak supervision and active learning, you can efficiently build a high-quality labeled dataset without having to manually label every sample. This can save a lot of time and resources while still producing a high-performing model.