How Active Learning Can Improve Your Computer Vision Pipeline

Traditional approaches might suggest randomly selecting images for annotation, but this can be inefficient and wasteful. Active Learning takes a more strategic approach: it identifies and prioritizes the most valuable samples that could contribute most significantly to improving model performance . Such a selection process considerably reduces the amount of labeled data needed to train a model while maintaining and improving the model accuracy.