PyTorch-implementation of the Siamese Subspace Clustering (SSCN) model proposed in the paper:

Learning Self-Expression Metrics for Scalable and Inductive Subspace Clustering
Julian Busch, Evgeniy Faerman, Matthias Schubert, and Thomas Seidl
NeurIPS 2020 Workshop: Self-Supervised Learning - Theory and Practice

Install the required packages specified in the file requirements.txt, e.g., using pip install -r requirements.txt. Additionally, the packages torch==1.6.0 and torchvision==0.7.0 are required and can be installed depending on your system and CUDA version following this guide: https://pytorch.org/get-started/locally/.

We provide a demonstration of the inner workings of our model on a small toy dataset. Please check out the notebook src/demo.ipynb.

• To run experiments or to reproduce the results reported in the paper, you can use the script src/run_experiment.py.
• Parameters need to be specified in a config-file in JSON-syntax. We uploaded the config-files used in our experiments into the folder config.
• Results will be tracked by MLflow. We uploaded the results from our runs which can be explored using the notebook src/evaluate_results.ipynb.
• Auto-encoders can be trained within the pipeline or pre-trained using the script src/pretrain_autoencoder.py. We uploaded the auto-encoder used in our runs into the folder trained_models.

If you use our model or any of the provided code or material, please cite our paper:

@article{busch2020learning,
  title={Learning Self-Expression Metrics for Scalable and Inductive Subspace Clustering},
  author={Busch, Julian and Faerman, Evgeniy and Schubert, Matthias and Seidl, Thomas},
  journal={NeurIPS 2020 Workshop: Self-Supervised Learning - Theory and Practice},
  year={2020}
}