Combine Active Learning and Optimal transport

Combining active learning and optimal transport can be a powerful way to build high-quality datasets. Here's a general process for doing this:

Define a cost function: Define a cost function that measures the dissimilarity between two samples. This can be done using various metrics, such as the Euclidean distance or a more complex distance metric.

Train a model on a small labeled dataset: Start with a small labeled dataset and train a model on it.

Use active learning to identify samples to label: Use active learning techniques to identify samples that the model is most uncertain about or that are likely to improve the model's performance if labeled.

Apply optimal transport to select the most informative samples: Use optimal transport techniques to select the most informative samples from the set of uncertain or potentially useful samples identified in step 3. Optimal transport can help to identify samples that are most likely to improve the model's performance, while taking into account the underlying structure of the data.

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 and optimal transport process to identify additional samples to label. Continue this process until you have labeled enough samples to train a high-performing model.

By combining active learning and optimal transport, you can efficiently build a high-quality labeled dataset that captures the underlying structure of the data. This can help to improve the performance of machine learning models, while reducing the amount of labeled data required to train them.