RiteeshGrandhi/sciclaimeval-shared-task huggingface dataset:
\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}