SMU_pytorch

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A Pytorch implementation of SMU: SMOOTH ACTIVATION FUNCTION FOR DEEP NETWORKS USING SMOOTHING MAXIMUM TECHNIQUE

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# SMU_pytorch
A Pytorch Implementation of SMU: SMOOTH ACTIVATION FUNCTION FOR DEEP NETWORKS USING SMOOTHING MAXIMUM TECHNIQUE

## arXiv
https://arxiv.org/abs/2111.04682

## Requirements
Pytorch 1.7

## Tensorflow version of SMU activation
Please check https://github.com/iFe1er/SMU for tensorflow 2.x implementation.

## Reference:
>@ARTICLE{2021arXiv211104682B,
>       author = {{Biswas}, Koushik and {Kumar}, Sandeep and {Banerjee}, Shilpak and {Pandey}, Ashish Kumar},
>        title = "{SMU: smooth activation function for deep networks using smoothing maximum technique}",
>      journal = {arXiv e-prints},
>     keywords = {Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Neural and Evolutionary Computing},
>         year = 2021,
>        month = nov,
>          eid = {arXiv:2111.04682},
>        pages = {arXiv:2111.04682},
>archivePrefix = {arXiv},
>       eprint = {2111.04682},
> primaryClass = {cs.LG},
>       adsurl = {https://ui.adsabs.harvard.edu/abs/2021arXiv211104682B},
>      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
>}

SMU_pytorch

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