RALF - Reinforced Active Learning Formulation. RALF is the framework used in  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 (, 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  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 ( Sec. 7.1). Finally, we provide the implementation of our RALF algorithm.
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References in zbMATH (referenced in 4 articles )
Showing results 1 to 4 of 4.
- Hino, Hideitsu: Active learning: problem settings and recent developments (2021)
- Ying-Peng Tang, Guo-Xiang Li, Sheng-Jun Huang: ALiPy: Active Learning in Python (2019) arXiv
- Rodner, Erik; Freytag, Alexander; Bodesheim, Paul; Fröhlich, Björn; Denzler, Joachim: Large-scale Gaussian process inference with generalized histogram intersection kernels for visual recognition tasks (2017)
- Long, Chengjiang; Hua, Gang; Kapoor, Ashish: A joint Gaussian process model for active visual recognition with expertise estimation in crowdsourcing (2016)