Fast Differentiable Matrix Sqrt Root

Overview

Fast Differentiable Matrix Sqrt Root

Geometric Interpretation of Matrix Square Root and Inverse Square Root

This repository constains the official Pytorch implementation of ICLR 22 paper "Fast Differentiable Matrix Square Root".

Usages

Check torch_utils.py for the implementation. Minimal exemplery usage is given as follows:

# Import and define function
from torch_utils import *
FastMatSqrt=MPA_Lya.apply
FastInvSqrt=MPA_Lya_Inv.apply

# For any batched matrices, compute their square root or inverse square root:
rand_matrix = torch.randn(5,32,32)
rand_cov = rand_matrix.bmm(rand_matrix.transpose(1,2))
rand_cov_sqrt = FastMatSqrt(rand_cov)
rand_inv_sqrt = FastInvSqrt(rand_cov)

Computer Vision Experiments

All the codes for the following experiments are available:

Citation

Please consider citing our paper if you think the code is helpful to your research.

@inproceedings{song2022fast,
  title={Fast Differentiable Matrix Square Root},
  author={Song, Yue and Sebe, Nicu and Wang, Wei},
  booktitle={ICLR},
  year={2022}
}

Contact

If you have any questions or suggestions, please feel free to contact me

[email protected]

Owner
YueSong
Ph.D. student in Computer Vision and Numerical Analysis
YueSong
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