Software & Data Downloads — GODS

One-class learning is the problem of fitting a model to data for which annotations are available only for a single class. Such models are useful for tasks such as anomaly detection, when the normal data is modeled by the 'one' class. In this software release, we are making public our implementation of our Generalized One-Class Discriminative Subspaces (GODS) algorithm (ICCV 2019, TPAMI 2023) for anomaly detection. The key idea of our method is to use a pair of orthonormal frames -- identifying the one-class data subspace -- to "sandwich" the labeled data via optimizing for two objectives jointly: i) minimize the distance between the origins of the two frames, and ii) to maximize the margin between the hyperplanes and the data. Our method demonstrates state-of-the-art results on several standard benchmarks.

• Related Publications

   Cherian, A., Wang, J., "Generalized One-Class Learning Using Pairs of Complementary Classifiers", IEEE Transactions on Pattern Analysis and Machine Intelligence, DOI: 10.1109/​TPAMI.2021.3092999, June 2021.

  BibTeX TR2021-076 PDF Software

  • @article{Cherian2021jun,
  • author = {Cherian, Anoop and Wang, Jue},
  • title = {Generalized One-Class Learning Using Pairs of Complementary Classifiers},
  • journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
  • year = 2021,
  • month = jun,
  • doi = {10.1109/TPAMI.2021.3092999},
  • url = {https://www.merl.com/publications/TR2021-076}
  • }

Access software at https://github.com/merlresearch/GODS.