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‎book.toml‎

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multilingual = false
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src = "doc"
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title = "DD-Ranking Benchmark"
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[output.html]
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mathjax-support = true

‎book/404.html‎

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‎book/datasets/overview.html‎

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‎book/getting-started/installation.html‎

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‎book/getting-started/quick-start.html‎

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‎book/index.html‎

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<script>
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<p>The evaluation method for DD-Ranking is grounded in the essence of dataset distillation, aiming to better reflect the information content of the synthesized data by assessing the following two aspects:</p>
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<ol>
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<p>The degree to which the original dataset is recovered under hard labels (hard label recovery): $\text{HLR} = \text{Acc}<em>{\text{full-hard}} - \text{Acc}</em>{\text{syn-hard}}$</p>
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<p>The degree to which the original dataset is recovered under hard labels (hard label recovery): \( \text{HLR} = \text{Acc.} \text{full-hard} - \text{Acc.} \text{syn-hard} \)</p>
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<p>The improvement over random selection when using personalized evaluation methods (improvement over random): $\text{IOR} = \text{Acc}<em>{\text{syn-any}} - \text{Acc}</em>{\text{rdm-any}}$</p>
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<p>The improvement over random selection when using personalized evaluation methods (improvement over random): \( \text{IOR} = \text{Acc.} \text{syn-any} - \text{Acc.} \text{rdm-any} \)</p>
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</li>
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<p>$\text{Acc.}$ is the accuracy of models trained on different samples. Samples' marks are as follows:</p>
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<p>\(\text{Acc.}\) is the accuracy of models trained on different samples. Samples' marks are as follows:</p>
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<ul>
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<li>$\text{full-hard}$: Full dataset with hard labels;</li>
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<li>$\text{syn-hard}$: Synthetic dataset with hard labels;</li>
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<li>$\text{syn-any}$: Synthetic dataset with personalized evaluation methods (hard or soft labels);</li>
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<li>$\text{rdm-any}$: Randomly selected dataset (under the same compression ratio) with the same personalized evaluation methods.</li>
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<li>\(\text{full-hard}\): Full dataset with hard labels;</li>
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<li>\(\text{syn-hard}\): Synthetic dataset with hard labels;</li>
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<li>\(\text{syn-any}\): Synthetic dataset with personalized evaluation methods (hard or soft labels);</li>
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<li>\(\text{rdm-any}\): Randomly selected dataset (under the same compression ratio) with the same personalized evaluation methods.</li>
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</ul>
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<p>To rank different methods, we combine the above two metrics as DD-Ranking Score:</p>
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<p>$$\text{DD-Ranking Score} = \frac{\text{IOR}}{\text{HLR}} = \frac{(\text{Acc}<em>{\text{syn-any}}-\text{Acc}</em>{\text{rdm-any}})}{(\text{Acc}<em>{\text{full-hard}}-\text{Acc}</em>{\text{syn-hard}})}$$</p>
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<p>\[\text{DD-Ranking Score} = \frac{\text{IOR}}{\text{HLR}} = \frac{(\text{Acc.} \text{syn-any}-\text{Acc.} \text{rdm-any})}{(\text{Acc.} \text{full-hard}-\text{Acc.} \text{syn-hard})}\]</p>
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‎book/introduction.html‎

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<p>The evaluation method for DD-Ranking is grounded in the essence of dataset distillation, aiming to better reflect the information content of the synthesized data by assessing the following two aspects:</p>
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<p>The degree to which the original dataset is recovered under hard labels (hard label recovery): $\text{HLR} = \text{Acc}<em>{\text{full-hard}} - \text{Acc}</em>{\text{syn-hard}}$</p>
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<p>The degree to which the original dataset is recovered under hard labels (hard label recovery): \( \text{HLR} = \text{Acc.} \text{full-hard} - \text{Acc.} \text{syn-hard} \)</p>
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</li>
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<p>The improvement over random selection when using personalized evaluation methods (improvement over random): $\text{IOR} = \text{Acc}<em>{\text{syn-any}} - \text{Acc}</em>{\text{rdm-any}}$</p>
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<p>The improvement over random selection when using personalized evaluation methods (improvement over random): \( \text{IOR} = \text{Acc.} \text{syn-any} - \text{Acc.} \text{rdm-any} \)</p>
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<p>$\text{Acc.}$ is the accuracy of models trained on different samples. Samples' marks are as follows:</p>
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<p>\(\text{Acc.}\) is the accuracy of models trained on different samples. Samples' marks are as follows:</p>
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<li>$\text{full-hard}$: Full dataset with hard labels;</li>
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<li>$\text{syn-hard}$: Synthetic dataset with hard labels;</li>
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<li>$\text{syn-any}$: Synthetic dataset with personalized evaluation methods (hard or soft labels);</li>
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<li>$\text{rdm-any}$: Randomly selected dataset (under the same compression ratio) with the same personalized evaluation methods.</li>
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<li>\(\text{full-hard}\): Full dataset with hard labels;</li>
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<li>\(\text{syn-hard}\): Synthetic dataset with hard labels;</li>
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<li>\(\text{syn-any}\): Synthetic dataset with personalized evaluation methods (hard or soft labels);</li>
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<p>To rank different methods, we combine the above two metrics as DD-Ranking Score:</p>
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<p>$$\text{DD-Ranking Score} = \frac{\text{IOR}}{\text{HLR}} = \frac{(\text{Acc}<em>{\text{syn-any}}-\text{Acc}</em>{\text{rdm-any}})}{(\text{Acc}<em>{\text{full-hard}}-\text{Acc}</em>{\text{syn-hard}})}$$</p>
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<p>\[\text{DD-Ranking Score} = \frac{\text{IOR}}{\text{HLR}} = \frac{(\text{Acc.} \text{syn-any}-\text{Acc.} \text{rdm-any})}{(\text{Acc.} \text{full-hard}-\text{Acc.} \text{syn-hard})}\]</p>
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‎book/metrics/general.html‎

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