- Start by cloning the repository.
- Run the startup.sh and install requirements found in requirements.txt (especially make sure torchvision version 0.2.2 is installed)
- Run ATRM through run.py. Feel free to change the "dset" argument. Current supported datasets are "MNIST", "CIFAR10", and "FASHIONMNIST."
- Set "show_plots" to True to visualize augmented images, distance histograms, and confusion matricies for accuracy.
- Add your own dataset by creating elif statements specific for your dataset in run.py, triplet.py, datasets.py, and/or networks.py. See the code for more details. You can copy what we did for the other datasets.
Access the paper here: http://openaccess.thecvf.com/content_ICCVW_2019/papers/GMDL/Nina_A_Decoder-Free_Approach_for_Unsupervised_Clustering_and_Manifold_Learning_with_ICCVW_2019_paper.pdf
BibTex
@inproceedings{nina2019decoder,
title={A Decoder-Free Approach for Unsupervised Clustering and Manifold Learning with Random Triplet Mining},
author={Nina, Oliver and Moody, Jamison and Milligan, Clarissa},
booktitle={Proceedings of the IEEE International Conference on Computer Vision Workshops},
pages={0--0},
year={2019}
}
@inproceedings{nina2019decoder,
title={A Decoder-Free Approach for Unsupervised Clustering and Manifold Learning with Random Triplet Mining},
author={Nina, Oliver and Moody, Jamison and Milligan, Clarissa},
booktitle={Proceedings of the IEEE International Conference on Computer Vision Workshops},
pages={0--0},
year={2019}
}