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be407a0 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 | \documentclass[varwidth=598pt]{standalone}
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\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth}
\begin{tabular}{ccccccc}
\toprule
\multicolumn{3}{c}{Config} & \multicolumn{4}{c}{Attack} \\
\cmidrule(r){1-3}\cmidrule(l){4-7}
Feature Extractor & Adversarial Train & Attack Generator & Clean & In & Out & In and Out \\
\midrule
Standard Trained & \color{green}\cmark & \color{red}\xmark & 0.706 & 0.263 & 0.147 & 0.029 \\
HAT & \color{red}\xmark & \color{red}\xmark & \textbf{0.947} & 0.741 & 0.720 & 0.511 \\
Not Used & \color{green}\cmark & \color{green}\cmark & 0.916 & 0.776 & 0.758 & 0.546 \\
Not Used & \color{green}\cmark & \color{red}\xmark & 0.923 & 0.789 & 0.771 & 0.560 \\
HAT & \color{green}\cmark & \color{red}\xmark & 0.943 & \textbf{0.837} & \textbf{0.862} & \textbf{0.693} \\
\bottomrule
\end{tabular}%
\end{adjustbox}
\caption{Table 3: Ablation study on our method. Other choices for the feature extractor, training method, and generated data attacking are tested on CIFAR-10, but none of them is as effective as the setting used in the ATD.}
\end{table}
\end{document} |