Model-Centric and Data-Centric Aspects of Active Learning for Neural Network Models.
Year: 2020
Type: preprint
Abstract: We study different data-centric and model-centric aspects of active learning with neural network models. i) We investigate incremental and cumulative training modes that specify how the currently labeled data are used for training. ii) Neural networks are models with a large capacity. Thus, we study how active learning depends on the number of epochs and neurons as well as the choice of batch size. iii) We analyze in detail the behavior of query strategies and their corresponding informativeness measures and accordingly propose more efficient querying and active learning paradigms. iv) We perform statistical analyses, e.g., on actively learned classes and test error estimation, that reveal several insights about active learning.
Source: arXiv (Cornell University)
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W3089003337