libact and deep-active-learning
About libact
ntucllab/libact
Pool-based active learning in Python
This tool helps data scientists and machine learning practitioners train more effective models with less labeled data. It takes your existing dataset, some of which is labeled and some unlabeled, and intelligently selects the most informative unlabeled examples for you to label. The output is a more accurate predictive model, built with fewer human labeling hours.
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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