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<a href="#">
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<img src="https://img.shields.io/badge/License-MIT-efefef">
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</a>
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<a href="https://arxiv.org/abs/2501.07930">
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<img src="https://img.shields.io/badge/arXiv-2501.07930-b31b1b.svg">
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<a href="https://thib-s.github.io/orthogonium/">
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<img alt="Documentation" src="https://img.shields.io/badge/Docs-here-0000ff">
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</a>
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- [Efficient Bound of Lipschitz Constant for Convolutional Layers by Gram Iteration](https://arxiv.org/abs/2305.16173)
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- [github of the two papers](https://github.com/blaisedelattre/lip4conv/tree/main)
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# 📖 Citations
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If you find this repository useful for your research, please cite:
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```
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@misc{boissin2025adaptiveorthogonalconvolutionscheme,
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title={An Adaptive Orthogonal Convolution Scheme for Efficient and Flexible CNN Architectures},
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author={Thibaut Boissin and Franck Mamalet and Thomas Fel and Agustin Martin Picard and Thomas Massena and Mathieu Serrurier},
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year={2025},
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eprint={2501.07930},
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archivePrefix={arXiv},
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primaryClass={cs.AI},
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url={https://arxiv.org/abs/2501.07930},
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}
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```
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# 🍻 Contributing
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This library is still in a very early stage, so expect some bugs and missing features. Also, before the version 1.0.0,

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