Source code for ICLR 2018 Paper: Active Learning for Convolutional Neural Networks: A Core-Set Approach
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additional_baselines iclr 2018 code Jun 12, 2018
coreset Update gurobi_solution_parser.py Oct 23, 2018
tf_base iclr 2018 code Jun 12, 2018
.gitignore Initial commit Jun 12, 2018
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README.md

Active Learning via Core-Sets

Source code for ICLR 2018 Paper: Active Learning for Convolutional Neural Networks: A Core-Set Approach

Main Organization of the Code

  • additional_baselines:
    • This folder includes baselines as well as pytorch implementation of the CIFAR-10 VGG network training code.
  • coreset
    • This folder includes the discrete optimization code which given feature emeddings, solves for core-sets. Its output chosen ids which is further used by learning code.

Training

Greedy Solver

Reference

If you find the code useful, please cite the following papers:

Active Learning for Convolutional Neural Networks: A Core-Set Approach. O. Sener, S. Savarese. International Conference on Learning Representations (ICLR), 2018. ()

@inproceedings{sener2018active,
    title={Active Learning for Convolutional Neural Networks: A Core-Set Approach},
    author={Ozan Sener and Silvio Savarese},
    booktitle={International Conference on Learning Representations},
    year={2018},
    url={https://openreview.net/forum?id=H1aIuk-RW},
}