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README.md

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Fair and benchmark for dataset distillation.
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<p align="center">
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| <a href="https://nus-hpc-ai-lab.github.io/DD-Ranking/"><b>Documentation</b></a> | <a href="https://huggingface.co/spaces/Soptq/DD-Ranking"><b>Leaderboard</b></a> | <a href=""><b>Paper </b> (Coming Soon)</a> | <a href=""><b>Twitter/X</b> (Coming Soon)</a> | <a href="https://join.slack.com/t/dd-ranking/shared_invite/zt-2xlcuq1mf-hmVcfrtqrIB3qXRjwgB03A"><b>Developer Slack</b></a> |
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| <a href="https://nus-hpc-ai-lab.github.io/DD-Ranking/"><b>Documentation</b></a> | <a href="https://huggingface.co/spaces/logits/DD-Ranking"><b>Leaderboard</b></a> | <a href=""><b>Paper </b> (Coming Soon)</a> | <a href=""><b>Twitter/X</b> (Coming Soon)</a> | <a href="https://join.slack.com/t/dd-ranking/shared_invite/zt-2xlcuq1mf-hmVcfrtqrIB3qXRjwgB03A"><b>Developer Slack</b></a> |
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</p>
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- [Ziheng Qin](https://henryqin1997.github.io/ziheng_qin/) (National University of Singapore)
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- [Kai Wang†](https://kaiwang960112.github.io/) (National University of Singapore)
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### Advisors:
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### Committee:
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- [Dai Liu](https://scholar.google.com/citations?user=3aWKpkQAAAAJ&hl=en) (Munich Technology University)
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- [Kaipeng Zhang](https://kpzhang93.github.io/) (Shanghai AI Lab)
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- [Jindong Gu](https://jindonggu.github.io/) (Oxford University)
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- [Mike Shou](https://sites.google.com/view/showlab) (National University of Singapore)
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- [Angela Yao](https://www.comp.nus.edu.sg/~ayao/) (National University of Singapore)
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- [Xavier Bresson](https://graphdeeplearning.github.io/authors/xavier-bresson/) (National University of Singapore)
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- [Tat-Seng Chua](https://www.chuatatseng.com/) (National University of Singapore)
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<!-- - [Xavier Bresson](https://graphdeeplearning.github.io/authors/xavier-bresson/) (National University of Singapore)
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- [Tat-Seng Chua](https://www.chuatatseng.com/) (National University of Singapore) -->
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- [Xindi Wu](https://xindiwu.github.io/) (Princeton University)
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- [Zhiwei Deng](https://lucas2012.github.io/) (Google DeepMind)
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- [Justin Cui](https://scholar.google.com/citations?user=zel3jUcAAAAJ&hl=en) (UC Los Angeles)

book/index.html

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</span>
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<span style="display: block; text-align: center;">
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<p><a href="https://github.com/NUS-HPC-AI-Lab/DD-Ranking"><img src="https://img.shields.io/github/stars/NUS-HPC-AI-Lab/DD-Ranking?style=flat&amp;logo=github" alt="GitHub stars" /></a>
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<a href="https://huggingface.co/spaces/Soptq/DD-Ranking"><img src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Leaderboard-yellow?style=flat" alt="Hugging Face" /></a>
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<a href="https://huggingface.co/spaces/logits/DD-Ranking"><img src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Leaderboard-yellow?style=flat" alt="Hugging Face" /></a>
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<a href="https://twitter.com/DD_Ranking"><img src="https://img.shields.io/badge/Twitter-Follow-blue?style=flat&amp;logo=twitter" alt="Twitter" /></a></p>
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</span>
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<p>Welcome to <strong>DD-Ranking</strong> (DD, <em>i.e.</em>, Dataset Distillation), an integrated and easy-to-use evaluation benchmark for dataset distillation! It aims to provide a fair evaluation scheme for DD methods that can decouple the impacts from knowledge distillation and data augmentation to reflect the real informativeness of the distilled data.</p>

book/introduction.html

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<span style="display: block; text-align: center;">
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<p><a href="https://github.com/NUS-HPC-AI-Lab/DD-Ranking"><img src="https://img.shields.io/github/stars/NUS-HPC-AI-Lab/DD-Ranking?style=flat&amp;logo=github" alt="GitHub stars" /></a>
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<a href="https://huggingface.co/spaces/Soptq/DD-Ranking"><img src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Leaderboard-yellow?style=flat" alt="Hugging Face" /></a>
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<a href="https://huggingface.co/spaces/logits/DD-Ranking"><img src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Leaderboard-yellow?style=flat" alt="Hugging Face" /></a>
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<a href="https://twitter.com/DD_Ranking"><img src="https://img.shields.io/badge/Twitter-Follow-blue?style=flat&amp;logo=twitter" alt="Twitter" /></a></p>
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</span>
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<p>Welcome to <strong>DD-Ranking</strong> (DD, <em>i.e.</em>, Dataset Distillation), an integrated and easy-to-use evaluation benchmark for dataset distillation! It aims to provide a fair evaluation scheme for DD methods that can decouple the impacts from knowledge distillation and data augmentation to reflect the real informativeness of the distilled data.</p>

book/print.html

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<p><a href="https://github.com/NUS-HPC-AI-Lab/DD-Ranking"><img src="https://img.shields.io/github/stars/NUS-HPC-AI-Lab/DD-Ranking?style=flat&amp;logo=github" alt="GitHub stars" /></a>
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<a href="https://huggingface.co/spaces/Soptq/DD-Ranking"><img src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Leaderboard-yellow?style=flat" alt="Hugging Face" /></a>
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<a href="https://huggingface.co/spaces/logits/DD-Ranking"><img src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Leaderboard-yellow?style=flat" alt="Hugging Face" /></a>
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<a href="https://twitter.com/DD_Ranking"><img src="https://img.shields.io/badge/Twitter-Follow-blue?style=flat&amp;logo=twitter" alt="Twitter" /></a></p>
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</span>
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<p>Welcome to <strong>DD-Ranking</strong> (DD, <em>i.e.</em>, Dataset Distillation), an integrated and easy-to-use evaluation benchmark for dataset distillation! It aims to provide a fair evaluation scheme for DD methods that can decouple the impacts from knowledge distillation and data augmentation to reflect the real informativeness of the distilled data.</p>

doc/introduction.md

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[![GitHub stars](https://img.shields.io/github/stars/NUS-HPC-AI-Lab/DD-Ranking?style=flat&logo=github)](https://github.com/NUS-HPC-AI-Lab/DD-Ranking)
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[![Hugging Face](https://img.shields.io/badge/🤗%20Hugging%20Face-Leaderboard-yellow?style=flat)](https://huggingface.co/spaces/Soptq/DD-Ranking)
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[![Hugging Face](https://img.shields.io/badge/🤗%20Hugging%20Face-Leaderboard-yellow?style=flat)](https://huggingface.co/spaces/logits/DD-Ranking)
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[![Twitter](https://img.shields.io/badge/Twitter-Follow-blue?style=flat&logo=twitter)](https://twitter.com/DD_Ranking)
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