---
license: other
license_name: mixed-see-provenance
license_link: https://doi.org/10.1007/978-3-032-31452-9_30
task_categories:
  - image-classification
tags:
  - privacy
  - pruning
  - debiasing
  - fairness
pretty_name: CutClean balanced datasets
---

# CutClean: balanced datasets

Data accompanying:

> Leonardo Magliolo, Vito Paolo Pastore, Giuseppe Valenzise, Enzo Tartaglione.
> **CutClean: Neural Network Pruning for Privacy-Preserving Inference.**
> *Pattern Recognition* — Proceedings of the 28th International Conference on Pattern
> Recognition (ICPR 2026), Lyon, France. Lecture Notes in Computer Science, Springer
> Nature Switzerland, pp. 450–465.
> [doi:10.1007/978-3-032-31452-9_30](https://doi.org/10.1007/978-3-032-31452-9_30)

Code: https://github.com/MaglioloLeonardo/CutClean

The datasets commonly used to benchmark model debiasing are built with a strong
correlation between the target and the private attribute. CutClean studies privacy leakage
that arises *independently* of such spurious correlations, so every dataset here is a custom
version balanced with respect to both the target and the private attribute.

## Contents

| File | Size | Content |
| --- | --- | --- |
| `corrupted_cifar10_unbiased.tar.gz` | 25 MB | 12,700 images, ten target classes, ten corruption types as private attribute |
| `waterbirds_unbiased.tar.gz` | 283 MB | 3,324 images, waterbird/landbird target, background as private attribute |
| `celeba_manifests/*.csv` | 1 MB | split manifests for the two CelebA configurations |
| `SHA256SUMS` | | checksums of the archives |

Both archives expand to one directory per split, containing one folder per target class.
The label and the private attribute are encoded in each file name as
`..._lbl<TARGET>_bias<PRIVATE>.png`.

```
corrupted_cifar_unbiased/{train,valid,test}/<class>/img_XXXXXX_lbl<Y>_bias<B>.png
waterbirds_unbiased/{train,val,test}/<class>/img_XXXXXX_id<N>_lbl<Y>_bias<B>.png
```

Split sizes follow the paper: 8,900 / 2,500 / 1,300 for Corrupted-CIFAR10 and
2,328 / 664 / 332 for Waterbirds.

## CelebA is not redistributed

The CelebA images are covered by a licence that restricts redistribution, so only the
manifests are published here. Each CSV lists, for seeds 0, 1 and 2, the exact balanced
subset used in the experiments:

| column | meaning |
| --- | --- |
| `seed` | seed of the balanced subsampling |
| `split` | `train`, `valid` or `test` |
| `image_id` | file name inside `img_align_celeba/` |
| `target` | target attribute (`Blond_Hair` or `Heavy_Makeup`) |
| `bias` | private attribute, gender |

Subset sizes match the paper: 5,548 / 728 / 720 for the blond-hair configuration and
812 / 36 / 88 for heavy make-up. The training code reproduces these subsets on its own from
the official CelebA release, which it downloads on first use; the manifests are provided so
the exact selection can be audited or pinned independently of the pandas version.

## Usage

```python
from huggingface_hub import snapshot_download
snapshot_download(repo_id="imDalton/cutclean-datasets", repo_type="dataset",
                  local_dir="data")
```

The reference implementation does this for you:

```bash
python src/data_setup.py --root ./data
```

## Provenance and licensing

These are derived datasets. The terms of the original sources apply, and each of them must
be consulted before use:

- **Corrupted-CIFAR10** is built from CIFAR-10 (Krizhevsky, 2009) by applying the corruption
  functions of Hendrycks and Dietterich, *Benchmarking Neural Network Robustness to Common
  Corruptions and Perturbations*, ICLR 2019 (Apache-2.0), following the protocol of Nam et
  al., *Learning from Failure*, NeurIPS 2020.
- **Waterbirds** is built from CUB-200-2011 (Wah et al., 2011) and Places (Zhou et al.,
  2017), following Sagawa et al., *Distributionally Robust Neural Networks*, ICLR 2020.
  CUB-200-2011 is made available for non-commercial research purposes only, and that
  restriction carries over to this derivative.
- **CelebA** (Liu et al., ICCV 2015) is available for non-commercial research purposes only.
  No CelebA image is redistributed here.

Redistribution is limited to non-commercial research use. If you are a rights holder and
consider that any material here should not be redistributed, please open a discussion on
this repository and it will be removed.

## Citation

```bibtex
@inproceedings{magliolo2026cutclean,
  author    = {Magliolo, Leonardo and Pastore, Vito Paolo and Valenzise, Giuseppe and Tartaglione, Enzo},
  title     = {CutClean: Neural Network Pruning for Privacy-Preserving Inference},
  booktitle = {Pattern Recognition -- 28th International Conference on Pattern Recognition, {ICPR} 2026, Lyon, France, August 17--22, 2026, Proceedings},
  series    = {Lecture Notes in Computer Science},
  publisher = {Springer Nature Switzerland},
  year      = {2026},
  pages     = {450--465},
  doi       = {10.1007/978-3-032-31452-9_30}
}
```