Learning-to-optimize-on-SPD-manifolds

This repository is the implementation of CVPR 2020 paper: "Learning to Optimize on SPD Manifolds".

We provide the code about the clustering task on the Kylberg texture dataset.

Prerequisites

Our code requires PyTorch v1.0 and Python 3.

Training our model.

We train the optimizer by

python train.py

You can modify config.py to set more detailed hyper-parameters.

The trained optimizer is saved in the snapshot folder.

Evaluate our model.

We evaluate the optimizer by

python evaluation.py

You can modify config_evaluation.py to set more detailed hyper-parameters.

Reference.

If this code is helpful, we'd appreciate it if you could cite our paper

@InProceedings{Gao_2020_CVPR,
author = {Gao, Zhi and Wu, Yuwei and Jia, Yunde and Harandi, Mehrtash},
title = {Learning to Optimize on SPD Manifolds},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2020}
}