Typiclust and ProbCover Official Code Repository

This is the official implementation for the papers Active Learning on a Budget - Opposite Strategies Suit High and Low Budgets and Active Learning Through a Covering Lens.

This code implements TypiClust and ProbCover - Simple and Effective Low Budget Active Learning methods.

Typiclust

Arxiv link, Twitter Post link, Blog Post link

TypiClust first employs a representation learning method, then clusters the data into K clusters, and selects the most Typical (Dense) sample from every cluster. In other words, TypiClust selects samples from dense and diverse regions of the data distribution.

Selection of 30 samples on CIFAR-10:

Selection of 10 samples from a GMM:

TypiClust Results summary

Probability Cover

Arxiv link, Twitter Post link, Blog Post link

ProbCover also uses a representation learning method. Then, around every point is placed a $\delta$-radius ball, and the subset of $b$ (budget) balls which covers the most of the points is selected, with their centers chosen as the samples to be labeled.

Unfolding selection of ProbCover

ProbCover results in the Semi-Supervised training framework

Usage

Please see USAGE for brief instructions on installation and basic usage examples.

Citing this Repository

This Repository makes use of two repositories: (SCAN and Deep-AL) Please consider citing their work and ours:

@article{hacohen2022active,
  title={Active learning on a budget: Opposite strategies suit high and low budgets},
  author={Hacohen, Guy and Dekel, Avihu and Weinshall, Daphna},
  journal={arXiv preprint arXiv:2202.02794},
  year={2022}
}

@article{yehudaActiveLearningCovering2022,
  title = {Active {{Learning Through}} a {{Covering Lens}}},
  author = {Yehuda, Ofer and Dekel, Avihu and Hacohen, Guy and Weinshall, Daphna},
  journal={arXiv preprint arXiv:2205.11320},
  year={2022}
}

License

This toolkit is released under the MIT license. Please see the LICENSE file for more information.

asdf2kr / TypiClust