A fully differentiable set autoencoder for encoding sets. Paper @KDD 2022.

The work is inspired by "The Set Autoencoder: Unsupervised Representation Learning for Sets ". The model makes use of an encoder from "Order Matters: Sequence to sequence for sets" and the decoder is a slightly modified version of the one in "The Set Autoencoder: Unsupervised Representation Learning for Sets ". To efficiently match the reconstructions of the autoencoder to their corresponding inputs to create a differentiable loss function, three architectures were developed and evaluated that could approximate the assignment problem and thus act as an end-to-end set matching network. The package includes code for these networks as well as baseline implementations of the set autoencoder fitted with the Hungarian matching algorithm and the Gale-Shapley algorithm.

Create a conda environment:

conda env create -f conda.yml

Activate the environment:

conda activate fdsa

Install:

pip install .

Install in editable mode for development:

pip install --user -e .

For some examples on how to use fdsa see here

If you use fdsa in your projects, please cite:

@inproceedings{10.1145/3534678.3539153,
  author = {Janakarajan, Nikita and Born, Jannis and Manica, Matteo},
  title = {A Fully Differentiable Set Autoencoder},
  year = {2022},
  isbn = {9781450393850},
  publisher = {Association for Computing Machinery},
  address = {New York, NY, USA},
  url = {https://doi.org/10.1145/3534678.3539153},
  doi = {10.1145/3534678.3539153},
  booktitle = {Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining},
  pages = {3061–3071},
  numpages = {11},
  keywords = {set matching network, multi-modality, autoencoders, sets},
  location = {Washington DC, USA},
  series = {KDD '22}
}