metadata

license: unknown
size_categories: 10K<n<100K
task_categories:
  - image-classification
pretty_name: Rademacher noise

Dataset Card for Rademacher noise for OOD Detection

Dataset Details

Dataset Description

Original Dataset Authors: [More Information Needed]
OOD Split Authors: Dan Hendrycks, Mantas Mazeika, Thomas Dietterich
Shared by: Eduardo Dadalto
License: unknown

Dataset Sources

Original Dataset Paper: [More Information Needed]
First OOD Application Paper: http://arxiv.org/abs/1812.04606v3

Direct Use

This dataset is intended to be used as an ouf-of-distribution dataset for image classification benchmarks.

Out-of-Scope Use

This dataset is not annotated.

Curation Rationale

The goal in curating and sharing this dataset to the HuggingFace Hub is to accelerate research and promote reproducibility in generalized Out-of-Distribution (OOD) detection.

Check the python library detectors if you are interested in OOD detection.

Personal and Sensitive Information

Please check original paper for details on the dataset.

Bias, Risks, and Limitations

Please check original paper for details on the dataset.

Citation

BibTeX:

@software{detectors2023,
author = {Eduardo Dadalto},
title = {Detectors: a Python Library for Generalized Out-Of-Distribution Detection},
url = {https://github.com/edadaltocg/detectors},
doi = {https://doi.org/10.5281/zenodo.7883596},
month = {5},
year = {2023}
}

@article{1812.04606v3,
author        = {Dan Hendrycks and Mantas Mazeika and Thomas Dietterich},
title         = {Deep Anomaly Detection with Outlier Exposure},
year          = {2018},
month         = {12},
note          = {ICLR 2019; PyTorch code available at
  https://github.com/hendrycks/outlier-exposure},
archiveprefix = {arXiv},
url           = {http://arxiv.org/abs/1812.04606v3}
}

Dataset Card Authors

Eduardo Dadalto

Dataset Card Contact

https://huggingface.co/edadaltocg