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Few-shot Learning as Cluster-induced Voronoi Diagrams (ICLR 2022)

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- RandStates_CUB_s1_q15_w5
- RandStates_CUB_s5_q15_w5
- __init__.py
- additional_transforms.py
- datamgr.py
- dataset.py
- feature_loader.py
- - download_CUB.sh
  - make_json.py
  - write_CUB_filelist.py
- demo_mnist.png
- table_3.png
- __init__.py
- baselinefinetune.py
- baselinetrain.py
- maml.py
- matchingnet.py
- meta_template.py
- protonet.py
- relationnet.py
- free_lunch_util.py
- run.py
.gitignore
backbone.py
configs.py
deepvoro.ipynb
evaluate_DC.py
extra_params.json
FSLTask.py
io_utils.py
LICENSE
README.md
requirements.txt
run_ens_checkI.sh
run_ens_checkII.sh
run_individual_vds.sh
run_save_plk_cub.sh
save_plk.py
utils.py
wrn_mixup_model.py

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<div align="center">
  <b>Few-shot Learning as Cluster-induced Voronoi Diagrams: A Geometric Approach</b>
</div>

<div align="center">
  The Tenth International Conference on Learning Representations (ICLR 2022)
</div>

<div align="center">
  <a href="https://openreview.net/forum?id=6kCiVaoQdx9">OpenReview</a>
</div>
<div align="center">
  <a href="https://arxiv.org/abs/2202.02471">arXiv</a>
</div>

## Introduction

In metric-based Few-Shot Learning (FSL), the classification model (e.g. Prototypical Networks and Logistic Regression) can be intepreted as either a [Voronoi diagram (VD)](https://en.wikipedia.org/wiki/Voronoi_diagram), or a [Power diagram (PD)](https://en.wikipedia.org/wiki/Power_diagram) constructed in the feature space.

In this work we dive deeper, geometrically, into this line and extend VD/PD to Cluster-induced Voronoi Diagrams (CIVD), proposed in [FOCS 2013](https://ieeexplore.ieee.org/document/6686175) and [SIAM Journal on Computing 2017](https://epubs.siam.org/doi/pdf/10.1137/15M1044874) ([PDF](https://ieeexplore.ieee.org/iel7/6685222/6686124/06686175.pdf?casa_token=GGuzxr8aLFIAAAAA:Rd0PS1RlLftuYLlDvmaKV9Y-FhKv9cZPmvADugH5YdREm5KgTWwTcDdVYqujrxI06-Pxi4RmCA)), and establish DeepVoro to combat the extreme data insufficiency in FSL.

<p align="center">
  <img src="./img/demo_mnist.png">
</p>

## Prerequisites

Install dependencies via:

```bash
pip install -r requirements.txt
```

Then please follow [S2M2_fewshot](https://github.com/nupurkmr9/S2M2_fewshot) to prepare the datasets and download the pretrained models, after which your directories should be like:

> checkpoints/miniImagenet/WideResNet28_10_S2M2_R/470.tar
> checkpoints/CUB/WideResNet28_10_S2M2_R/255.tar
> checkpoints/tieredImageNet/WideResNet28_10_S2M2_R/199.tar

## Reproducing the results

In this tutorial we use CUB dataset, the smallest among three, to illustrate the DeepVoro workflow (there should have been a directory "filelists/CUB/CUB_200_2011" storing the CUB images, after the previous step).

**Step 1**: Feature extraction:
```bash
bash run_save_plk_cub.sh
```

**Step 2**: Establish individual VDs:
```bash
bash run_individual_vds.sh
```

**Step 3**: Establish CIVD: see jupyter notebook "deepvoro.ipynb" for details; you should be able to reproduce the following results:

<p align="center">
  <img src="./img/table_3.png">
</p>

## References

If you find the software useful please consider citing:

```
@misc{ma2022fewshot,
      title={Few-shot Learning as Cluster-induced Voronoi Diagrams: A Geometric Approach},
      author={Chunwei Ma and Ziyun Huang and Mingchen Gao and Jinhui Xu},
      year={2022},
      eprint={2202.02471},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}
```

or

```
@inproceedings{ma2022fewshot,
    title={Few-shot Learning via Dirichlet Tessellation Ensemble},
    author={Chunwei Ma and Ziyun Huang and Mingchen Gao and Jinhui Xu},
    booktitle={International Conference on Learning Representations},
    year={2022},
    url={https://openreview.net/forum?id=6kCiVaoQdx9}
}
```

**Acknowledgments**

In this project we use (parts of) the official implementations of the following works:

* [S2M2_fewshot](https://github.com/nupurkmr9/S2M2_fewshot)
* [simple_shot](https://github.com/mileyan/simple_shot)
* [Few_Shot_Distribution_Calibration](https://github.com/ShuoYang-1998/Few_Shot_Distribution_Calibration)

We thank the respective authors for open-sourcing their methods, which makes this work possible.

If you have any problem please [contact me](mailto:chunweim@buffalo.edu).