metadata_archaeology

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metadata_archaeology

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Official code for the paper: "Metadata Archaeology"

metadata_archaeology 최신버젼 다운로드

최종 버전 다운로드 (.zip)

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# Metadata Archaeology [ICLR'23 Spotlight]

A PyTorch implementation for the ***ICLR'23*** paper [***Metadata Archaeology: Unearthing Data Subsets by Leveraging Training Dynamics***](https://arxiv.org/abs/2209.10015).

Check the [***OpenReview***](https://openreview.net/forum?id=PvLnIaJbt9) page for detailed discussions on the paper.

## Execution

The code can be executed using the ```train.sh``` script provided.
The code also requires memorization/consistency scores to compute typical/atypical examples.
Configure the ```data_dir``` variable in the script to define the path to these scores.

We use consistency scores by default. Consistency scores can be downloaded from the official website: ```https://pluskid.github.io/structural-regularity/```

Similarly, memorization scores can also be downloaded from the official website: ```https://pluskid.github.io/influence-memorization/```

## Surfaced Examples

Surfaced examples can be accessed through [***Google Drive***](https://drive.google.com/drive/folders/1TXGEHBNxcRUFm-qR-I2241IOgnAmqcVR).
Also checkout our [***webpage***](https://metadata-archaeology.github.io/) for more details.

## Loss Trajectories

As loss trajectories themselves as of great interest, the computed loss trajectories on CIFAR-10, CIFAR-100 and ImageNet are directly available for download through 
[***Google Drive***](https://drive.google.com/drive/folders/1Hds32eyIuGSJd1e6ZndC6OEcwfFWz4vK?usp=share_link).
Please refer to the main script ```metadata_archaeology.py``` for details regarding how to load these files.

## Citation

```
@article{siddiqui2022metadataarchaeology,
  title={Metadata Archaeology: Unearthing Data Subsets by Leveraging Training Dynamics},
  author={Siddiqui, Shoaib Ahmed and Rajkumar, Nitarshan and Maharaj, Tegan and Krueger, David and Hooker, Sara},
  journal={arXiv preprint},
  year={2022},
  url={https://arxiv.org/abs/2209.10015}
}
```

## Issues/Feedback

In case of any issues, feel free to drop me an email or open an issue on the repository.

Email: **msas3@cam.ac.uk**

## License

MIT

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