Neighborhood Reconstructing Autoencoders

The official repository for (Lee, Kwon, and Park, NeurIPS 2021).

This paper proposes Neighborhood Reconstructing Autoencoders (NRAE), which is a graph-based autoencoder that explicitly accounts for the local connectivity and geometry of the data, and consequently learns a more accurate data manifold and representation.

• Paper
• [15-mins video: TBU]
• Slides
• Poster
• OpenReview

Preview (synthetic data)

Preview (rotated/shifted MNIST)

Environment

The project is developed under a standard PyTorch environment.

• python 3.8.8
• numpy
• matplotlib
• imageio
• argparse
• yaml
• omegaconf
• torch 1.8.0
• CUDA 11.1

Running

python train_{X}.py --config configs/{A}_{B}_{C}.yml --device 0

• X is either synthetic or MNIST
• A is either AE, NRAEL, or NRAEQ
• B is either toy or mnist
• If B is toy, then C is either denoising or geometry_preserving. Elseif B is mnist, then C is either rotated or shifted.

Playing with the code

• The most important parameters requiring tuning include: i) the number of nearest neighbors for graph construction num_nn and ii) kernel parameter lambda (you can find these parameters in configs/NRAEL_toy_denoising.yml for example).
• We empirically observe that setting as include_center=True (when defining data loader) has performance advantange.
• You can add a new type of 2d synthetic dataset in loader.synthetic_dataset.SyntheticData.get_data (currently, we have sincurve and swiss_roll).

Citation

If you found this library useful in your research, please consider citing:

@article{lee2021neighborhood,
  title={Neighborhood Reconstructing Autoencoders},
  author={Lee, Yonghyeon and Kwon, Hyeokjun and Park, Frank},
  journal={Advances in Neural Information Processing Systems},
  volume={34},
  year={2021}
}