Metadata-Version: 2.1
Name: sbo
Version: 1.0.5
Summary: SBO defines an iterative approach to translate points by a most likely distance from a given dataset
Home-page: UNKNOWN
Author: Felix
Author-email: 10831300+flxai@users.noreply.github.com
License: UNKNOWN
Project-URL: Source Code, https://github.com/flxai/soft-brownian-offset
Project-URL: Bug Tracker, https://github.com/flxai/soft-brownian-offset/issues
Project-URL: Documentation, https://soft-brownian-offset.readthedocs.io
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](https://pypi.org/project/sbo/) 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:
        
        ```python
        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](https://soft-brownian-offset.readthedocs.io/en/latest/).
        
        ## 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 available [as open access from the CVF](https://openaccess.thecvf.com/content/CVPR2021W/SAIAD/papers/Moller_Out-of-Distribution_Detection_and_Generation_Using_Soft_Brownian_Offset_Sampling_and_CVPRW_2021_paper.pdf).
        For citations please see [*cite*](#cite).
        
        ## Demonstration
        
        See the following plot to gain intuition on the approach's results:
        
        ![demonstration](docs/img/sbo-demo.svg)
        
        Please see the [documentation](https://soft-brownian-offset.readthedocs.io/en/latest/#demonstration) for the source code to recreate the plot.
        
        ## Cite
        
        Please cite SBO in your paper if it helps your research:
        
        ```bibtex
        @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
        }
        ```
        
        
Platform: UNKNOWN
Description-Content-Type: text/markdown
