Active Learning

Active Learning PBCA default

This workflow shows an example of Active Learning. We read a simple dataset of images separated in two classes and calculate some features on them. Now the "Active Learning Loop" determines the best sample which could be manuallay labeled by a user and benefits most to the separation of the classes. The decision of the best sample is based on a specific score (in this case a "PBAC Scorer"). The samples can be labeled using the view of the "Active Learn Loop End". Execute the loop and open the view while the loop is executing.

Active Learning PBCA modular Score

This workflow shows an example of Active Learning. We read a simple dataset of images separated in two classes and calculate some features on them. Now the Active Learning Loop determines the best sample which could be manuallay labeled by a user and benefits most to the separation of the calsses. The decision of the best sample is based on a specific score. Here we use a modular score calculation approach in order to find the best sample.

Active Learning Uncertainty Sampling

This workflow shows an example of Active Learning. In this example we use scoring based on a previous prediction. and use an "Auto Active Learn Loop End" in order to choose the class for the best scoring sample. One can easily replace the "Auto Active Learn Loop End" with a default "Active Learn Loop End" to manually label the data. The "Auto Active Learn Loop End" should only be used for demonstration or benchmark purposes.

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