dkohlsdorf / dti-clustering

(NeurIPS 2020 oral) Code for "Deep Transformation-Invariant Clustering" paper

dti-clustering

Pytorch implementation of "Deep Transformation-Invariant Clustering" paper:

Preprint | Project webpage

Installation

1. Create conda environment

conda env create -f environment.yml
conda activate dtic

Optional: some monitoring routines are implemented, you can use them by specifying the visdom port in the config file. You will need to install visdom from source beforehand

git clone https://github.com/facebookresearch/visdom
cd visdom && pip install -e .

2. Download non-torchvision datasets

./download_data.sh

How to use 🚀

1. Launch a training

cuda=gpu_id config=filename.yml tag=run_tag ./pipeline.sh

where:

• gpu_id is a target cuda device id,
• filename.yml is a YAML config located in configs folder,
• run_tag is a tag for the experiment.

Results are saved at runs/${DATASET}/${DATE}_${run_tag} where DATASET is the dataset name specified in filename.yml and DATE is the current date in mmdd format. Some training visual results like prototype evolutions and transformation prediction examples will be saved. Here is an example of learned MNIST prototypes and transformation predictions for a given query image:

2. Reproduce our quantitative results on MNIST-test (10 runs)

cuda=gpu_id config=mnist_test.yml tag=dtikmeans ./multi_pipeline.sh

Switch the model name to dtigmm in the config file to reproduce results for DTI GMM. Available configs are:

• affnist_test.yml
• fashion_mnist.yml
• frgc.yml
• mnist.yml
• mnist_1k.yml
• mnist_color.yml
• mnist_test.yml
• svhn.yml
• usps.yml

3. Reproduce our qualitative results on Instagram collections

1. Create a santaphoto dataset by running process_insta_santa.sh script. It can take a while to scrape the 10k posts from Instagram.
2. Launch training with cuda=gpu_id config=instagram.yml tag=santaphoto ./pipeline.sh

That's it!

How to cite? 📋

If you find this work useful in your research, please consider citing:

@inproceedings{monnier2020dticlustering,
  title={{Deep Transformation-Invariant Clustering}},
  author={Monnier, Tom and Groueix, Thibault and Aubry, Mathieu},
  booktitle={NeurIPS},
  year={2020},
}