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<!DOCTYPE html>
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<title>Awesome Dataset Distillation</title>
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<div class="show-on-large-screens"><h1 class="title display-large">Awesome Dataset Distillation</h1></div>
<div class="show-on-middle-screens"><h1 class="title display-large">Awesome<br> Dataset Distillation</h1></div>
<div class="show-on-small-screens"><h1 class="title display-large">Awesome<br> Dataset<br> Distillation</h1></div>
<div><p class="brief title-large">Awesome Dataset Distillation provides the most comprehensive and detailed information on the Dataset Distillation field.</p></div>
<div><p class="brief title-large">This project is curated and maintained by <a style="color: black;" href="https://www-lmd.ist.hokudai.ac.jp/member/guang-li/">Guang Li</a>, <a style="color: black;" href="https://www.bozhao.me/">Bo Zhao</a>, and <a style="color: black;" href="https://www.tongzhouwang.info/">Tongzhou Wang</a>.</p></div>
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Awesome Dataset Distillation
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<p class="count-number on-background-text display-large">198</p>
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<h2 class="section-title display-medium on-background-text">Background & Vision</h2>
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<p class="background-vision body-large on-surface-variant-text">Dataset distillation is the task of synthesizing a small dataset such that models trained on it achieve high performance on the original large dataset. A dataset distillation algorithm takes as input a large real dataset to be distilled (training set), and outputs a small synthetic distilled dataset, which is evaluated via testing models trained on this distilled dataset on a separate real dataset (validation/test set). A good small distilled dataset is not only useful in dataset understanding, but has various applications (e.g., continual learning, privacy, neural architecture search, etc.). This task was first introduced in the paper <a class="body-large on-surface-variant-text" href="https://www.tongzhouwang.info/dataset_distillation/">Dataset Distillation [Tongzhou Wang et al., '18]</a>, along with a proposed algorithm using backpropagation through optimization steps. Then the task was first extended to the real-world datasets in the paper <a class="body-large on-surface-variant-text" href="https://arxiv.org/abs/2104.02857">Medical Dataset Distillation [Guang Li et al., '19]</a>, which also explored the privacy preservation possibilities of dataset distillation. In the paper <a class="body-large on-surface-variant-text" href="https://arxiv.org/abs/2006.05929">Dataset Condensation [Bo Zhao et al., '20]</a>, gradient matching was first introduced and greatly promoted the development of the dataset distillation field.</p>
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<p class="background-vision body-large on-surface-variant-text">In recent years (2022-now), dataset distillation has gained increasing attention in the research community, across many institutes and labs. More papers are now being published each year. These wonderful researches have been constantly improving dataset distillation and exploring its various variants and applications.</p>
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<h2 class="section-title display-medium on-background-text">Latest Updates</h2>
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<p class="essay-content title-large on-surface-variant-text">2024/11/10</p>
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<p class="essay-content">Fetch and Forge: Efficient Dataset Condensation for Object Detection</p>
<p class="title-medium essay-author"><i>Ding Qi et al., NeurIPS 2024</i></p>
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<p class="essay-content"><a class="on-surface-variant-text" href="https://github.com/KeViNYuAn0314/AutoPalette">Color-Oriented Redundancy Reduction in Dataset Distillation</a></p>
<p class="title-medium essay-author"><i>Bowen Yuan et al., NeurIPS 2024</i></p>
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<a href="https://arxiv.org/abs/2408.14506">
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<p class="essay-content title-large on-surface-variant-text">2024/08/24</p>
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<p class="essay-content">Adaptive Backdoor Attacks Against Dataset Distillation for Federated Learning</p>
<p class="title-medium essay-author"><i>Ze Chai et al., ICC 2024</i></p>
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<a href="https://ieeexplore.ieee.org/abstract/document/10622462?casa_token=tHyZ-Pz7DpUAAAAA:vmCYI4cUcKzMluUsASHhIhr0CvBkjzBR-0N7REVj7aFN5hT5TinQTpSEsE0Bo3Fl8auh52Fipm_v">
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<p class="essay-content title-large on-surface-variant-text">2024/08/23</p>
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<p class="essay-content">Not All Samples Should Be Utilized Equally: Towards Understanding and Improving Dataset Distillation</p>
<p class="title-medium essay-author"><i>Shaobo Wang et al., 2024</i></p>
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<a href="https://arxiv.org/abs/2408.12483">
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<p class="essay-content title-large on-surface-variant-text">2024/08/22</p>
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<p class="essay-content">Dataset Condensation with Latent Quantile Matching</p>
<p class="title-medium essay-author"><i>Wei Wei et al., CVPR 2024 Workshop</i></p>
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<p class="essay-content title-large on-surface-variant-text">2024/08/20</p>
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<p class="title-medium essay-author"><i>Duo Su & Junjie Hou & Guang Li et al., ECCV 2024 Workshop</i></p>
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<a href="https://arxiv.org/abs/2408.08610">
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<p class="essay-content title-large on-surface-variant-text">2024/08/07</p>
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<p class="title-medium essay-author"><i>Zekai Li & Ziyao Guo et al., 2024</i></p>
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<a href="https://arxiv.org/abs/2408.03360">
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<p class="essay-content title-large on-surface-variant-text">2024/08/02</p>
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<a href="https://arxiv.org/abs/2407.20299">
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<p class="essay-content title-large on-surface-variant-text">2024/07/29</p>
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<a href="https://arxiv.org/abs/2404.18962">
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<p class="essay-content title-large on-surface-variant-text">2024/07/25</p>
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<p class="essay-content">Dataset Distillation in Medical Imaging: A Feasibility Study</p>
<p class="title-medium essay-author"><i>Muyang Li et al., 2024</i></p>
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<p class="essay-content">A Theoretical Study of Dataset Distillation</p>
<p class="title-medium essay-author"><i>Zachary Izzo et al., NeurIPS 2023 Workshop</i></p>
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<p class="essay-content title-large on-surface-variant-text">2024/07/23</p>
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<a href="https://arxiv.org/abs/2407.14245">
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<p class="essay-content title-large on-surface-variant-text">2024/07/16</p>
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<a href="https://cvlab.yonsei.ac.kr/projects/FYI/">
<span class="essay-button surface">
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