SBO defines an iterative approach to translate points by a most likely distance from a given dataset
Project description
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 allows for trivial OOD generation -- as shown above -- or more complex schemes that apply the transformation of learned representations. For an in-depth look at the latter please refer to the paper that is also available as a pre-print on arXiv. 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:
@inproceedings{MBH21,
author = {Möller, Felix and Botache, Diego and Huseljic, Denis and Heidecker, Florian and Bieshaar, Maarten and Sick, Bernhard},
booktitle = {{Proc. of CVPR SAIAD Workshop}},
title = {{Out-of-distribution Detection and Generation using Soft Brownian Offset Sampling and Autoencoders}},
year = 2021
}
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