https://github.com/paccmann/fdsa
A fully differentiable set autoencoder
https://github.com/paccmann/fdsa
deep-learning multimodal-data set-autoencoder
Last synced: 2 months ago
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A fully differentiable set autoencoder
- Host: GitHub
- URL: https://github.com/paccmann/fdsa
- Owner: PaccMann
- License: mit
- Created: 2021-02-25T14:52:40.000Z (over 4 years ago)
- Default Branch: main
- Last Pushed: 2024-04-03T14:55:46.000Z (about 1 year ago)
- Last Synced: 2025-03-24T16:53:01.389Z (3 months ago)
- Topics: deep-learning, multimodal-data, set-autoencoder
- Language: Python
- Homepage:
- Size: 6.1 MB
- Stars: 17
- Watchers: 4
- Forks: 3
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
[](https://opensource.org/licenses/MIT)
# Fully Differentiable Set Autoencoder (fdsa)
A fully differentiable set autoencoder for encoding sets. [Paper @KDD 2022](https://dl.acm.org/doi/10.1145/3534678.3539153).
The work is inspired by ["The Set Autoencoder: Unsupervised Representation Learning for Sets "](https://openreview.net/forum?id=r1tJKuyRZ). The model makes use of an
encoder from ["Order Matters: Sequence to sequence for sets"](https://arxiv.org/abs/1511.06391) and the decoder is a slightly modified version of the one in ["The Set Autoencoder: Unsupervised Representation Learning for Sets "](https://openreview.net/forum?id=r1tJKuyRZ). 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.
## Installation
Create a conda environment:
```console
conda env create -f conda.yml
```
Activate the environment:
```console
conda activate fdsa
```
Install:
```console
pip install .
```
### development
Install in editable mode for development:
```sh
pip install --user -e .
```
## Examples
For some examples on how to use `fdsa` see [here](./examples)
## Citation
If you use `fdsa` in your projects, please cite:
```bib
@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}
}
```