swMATH ID: 33955
Software Authors: Sandra Ebert, Mario Fritz, Bernt Schiele
Description: RALF - Reinforced Active Learning Formulation. RALF is the framework used in [1] and part of the project Semi-supervised learning in image collections. This framework combines active learning and reinforcement learning to enable a time-varying trade-off among different exploration and exploitation sampling criteria that is learned online during the sampling process. In the following framework, we provide different sampling criteria for exploration as well as exploitation. We propose a novel exploration criteria graph density ([1], Sec. 3.2) that consistently outperforms previous exploration criteria for label propagation as well as other algorithms such as SVM or KNN. More recently, we show in [2] that this criteria also helps to find more representative labels for metric learning in comparison to the other one. Additionally, we make available our implementation of the previous method with our improvements ([1] Sec. 7.1). Finally, we provide the implementation of our RALF algorithm.
Homepage: https://www.mpi-inf.mpg.de/departments/computer-vision-and-machine-learning/research/knowledge-transfer-and-semi-supervised-learning/ralf-reinforced-active-learning-formulation/
Related Software: AlexNet; ImageNet; Python; ALiPy; BatchBALD; Spearmint; DeCAF; LIBLINEAR; LIBSVM; LBFGS-B; L-BFGS; L-BFGS-B
Cited in: 3 Documents

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