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Picking groups instead of samples: a close look at Static Pool-based Meta-Active Learning
Mas, I.; Morros, J.R.; Vilaplana, V.
Type of activity
Presentation of work at congresses
Name of edition
IEEE International Conference on Computer Vision 2019
Date of publication
Book of congress proceedings
2019 International Conference on Computer Vision ICCV 2019: proceedings: 27 October - 2 November 2019 Seoul, Korea
Institute of Electrical and Electronics Engineers (IEEE)
Multimodal Signal Processing and Machine Learning on Graphs
http://hdl.handle.net/2117/179428 https://imatge.upc.edu/web/publications/picking-groups-instead-samples-close-look-static-pool-based-meta-active-learning URL
Active Learning techniques are used to tackle learning problems where obtaining training labels is costly. In this work we use Meta-Active Learning to learn to select a subset of samples from a pool of unsupervised input for further annotation. This scenario is called Static Pool-based Meta-Active Learning. We propose to extend existing approaches by performing the selection in a manner that, unlike previous works, can handle the selection of each sample based on the whole selected subset.
Mas, I.; Morros, J.R.; Vilaplana, V. Picking groups instead of samples: a close look at Static Pool-based Meta-Active Learning. A: IEEE International Conference on Computer Vision Workshops. "2019 International Conference on Computer Vision ICCV 2019: proceedings: 27 October- 2 November 2019 Seoul, Korea". Institute of Electrical and Electronics Engineers (IEEE), 2019, p. 1-9.
Active learning, Few shot learning, Learning under constraints, Meta active learning, Meta learning, RNN, Reinforcement learning, Static pool based active learning
Group of research
GPI - Image and Video Processing Group IDEAI-UPC - Intelligent Data Science and Artificial Intelligence Research Center