soft-brownian-offset

Soft Brownian Offset (SBO) defines an iterative approach to translate points by a most likely distance from a given dataset. It can be used for generating out-of-distribution samples.

Installation

This project is hosted on PyPI and can therefore be installed easily through pip:

pip install sbo

Dependending on your setup you may need to add --user after the install.

Usage

For brevity's sake here's a short introduction to the library's usage:

from sklearn.datasets import make_moons
from sbo import soft_brownian_offset

X, _ = make_moons(n_samples=60, noise=.08)
X_ood = soft_brownian_offset(X, d_min=.35, d_off=.24, n_samples=120, softness=0)

For more details please see the documentation.

Background

The technique is described in detail within the paper TBA. For citations please see cite.

Demonstration

See the following plot to gain intuition on the approach's results:

Please see the documentation for the source code to recreate the plot.

Cite

Please cite SBO in your paper if it helps your research TBA:

@article{name2020sbo,
  Author = {TBA},
  Journal = {arXiv preprint arXiv:TBA},
  Title = {TBA},
  Year = {2020}
}