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.ymlActivate the environment:
conda activate fdsaInstall:
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}
}