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

• Training code uses http://torch.ch/blog/2015/07/30/cifar.html

Greedy Solver

• If you only need a greedy solver, you can use https://github.com/google/active-learning/blob/master/sampling_methods/kcenter_greedy.py

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},
}