Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networks
Paper
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1706.02690
•
Published
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This dataset is intended to be used as an ouf-of-distribution dataset for image classification benchmarks.
This dataset is not annotated.
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.
Please check original paper for details on the dataset.
Please check original paper for details on the dataset.
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{1706.02690v5,
author = {Shiyu Liang and Yixuan Li and R. Srikant},
title = {Enhancing The Reliability of Out-of-distribution Image Detection in
Neural Networks},
year = {2017},
month = {6},
note = {ICLR 2018},
archiveprefix = {arXiv},
url = {http://arxiv.org/abs/1706.02690v5}
}
@article{1507.01422v1,
author = {Junting Pan and Xavier Giró-i-Nieto},
title = {End-to-end Convolutional Network for Saliency Prediction},
year = {2015},
month = {7},
note = {Winner of the saliency prediction challenge in the Large-scale Scene
Understanding (LSUN) Challenge in the associated workshop of the IEEE
Conference on Computer Vision and Pattern Recognition (CVPR) 2015},
archiveprefix = {arXiv},
url = {http://arxiv.org/abs/1507.01422v1}
}
Eduardo Dadalto