Active Learning for Convolutional Neural Networks: A Core-Set Approach

2018 inproceedings sener2018coreset ○ Not yet verified

Authors

Ozan Sener, Silvio Savarese

Venue

International Conference on Learning Representations (ICLR)

Abstract

Defines active learning as core-set selection, choosing points such that a model trained on the subset is competitive for remaining data. Provides theoretical bounds via k-Center problem.

BibTeX

Local Entry

@inproceedings{sener2018coreset,
  title = {Active Learning for Convolutional Neural Networks: A Core-Set Approach},
  author = {Ozan Sener and Silvio Savarese},
  year = {2018},
  booktitle = {International Conference on Learning Representations (ICLR)},
  url = {https://arxiv.org/abs/1708.00489},
  abstract = {Defines active learning as core-set selection, choosing points such that a model trained on the subset is competitive for remaining data. Provides theoretical bounds via k-Center problem.}
}

From OPENALEX

@inproceedings{sener2018coreset,
  title = {Active Learning for Convolutional Neural Networks: A Core-Set Approach},
  author = {Ozan Şener and Silvio Savarese},
  year = {2017},
  booktitle = {arXiv (Cornell University)},
  doi = {10.48550/arxiv.1708.00489}
}