scikit-activeml and deep-active-learning
About scikit-activeml
scikit-activeml/scikit-activeml
scikit-activeml: A Comprehensive and User-friendly Active Learning Library
This library helps machine learning practitioners efficiently train models when labeled data is scarce or expensive to obtain. You provide a large amount of unlabeled data and a small initial set of labeled data. The system intelligently selects the most informative data points for you to label, resulting in a high-performing model with minimal labeling effort. Data scientists and ML engineers working with limited labeling budgets would find this valuable.
About deep-active-learning
cure-lab/deep-active-learning
An implementation of the state-of-the-art Deep Active Learning algorithms
This project offers various strategies for 'active learning' with deep neural networks. It helps data scientists and machine learning engineers reduce the cost of labeling large datasets by intelligently selecting the most informative data points to be annotated. You provide a deep learning model and your unlabeled data, and it outputs a prioritized list of data points that will give the most bang for your buck in terms of labeling effort.
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