
\begin{table*}[t]
    \caption{Results of the domain adaptation from \acl{K19} (source) to \acl{K16} (target). 1\% of the source domain labels are known and the target domain labels are unknown. The top results for the domain adaptation methods are highlighted in bold. 
    %We report the F1 and weighted F1 score for classes and average (all), respectively.
    We report the F1 score for each class, as well as the overall F1 score.}
    %\begin{center}
    \label{tbl:cross-domain}
    \centering
    \begin{footnotesize}
    \begin{threeparttable}
    \begin{tabular}{l l c c c c c c c c c}
    \toprule
    \toprule
    Methods & TUM & STR & LYM & DEB & NORM & ADI & BACK & ALL\\
    \midrule
    Source only$^{\ddagger}$ & 74.0\tnote{**} & 77.4\tnote{**} & 75.3\tnote{**} & 50.5\tnote{**} & 66.9\tnote{**} & 87.0\tnote{**} & 93.1\tnote{**} & 75.1\tnote{**} \\
    Stain norm. \cite{macenko2009method} & 77.8\tnote{**} & 75.9\tnote{**} & 68.2\tnote{**} & 42.1\tnote{**} & 75.1\tnote{**} & 77.4\tnote{**} & 87.6\tnote{**} & 72.2\tnote{**} \\
    CylceGAN \cite{zhu2017unpaired} & 70.7\tnote{**} & 71.6\tnote{**} & 62.3\tnote{**} & 47.6\tnote{**} & 75.5\tnote{**} & 89.0\tnote{**} & 88.2\tnote{**} & 72.4\tnote{**} \\
    DANN \cite{ganin2015unsupervised} & 65.8\tnote{**} & 60.8\tnote{**} & 42.3\tnote{**} & 47.8\tnote{**} & 61.9\tnote{**} & 64.1\tnote{**} & 62.3\tnote{**} & 57.8\tnote{**}\\
    % \christian{DANN} \cite{ganin2015unsupervised} & 72.9 & 70.8 & 72.6 & 58.0 & 71.7 & 90.8 & 94.5 & 76.2 \\
    SelfPath \cite{koohbanani2020self}& 71.5\tnote{**} & 68.8\tnote{**} & 68.1\tnote{**} & 57.6\tnote{**} & 77.6\tnote{**} & 82.3\tnote{**} & 85.5\tnote{**} & 73.1\tnote{**} \\
    % \christian{SelfPath} \cite{koohbanani2020self}& 73.1 & 70.5 & 69.7 & 54.1 & 69.7 & 86.5 & 94.4 & 74.2 \\
    OSDA \cite{saito2018open} & 82.0\tnote{**} & 78.2\tnote{*} & \textbf{83.6} & 63.8\tnote{**} & 80.3\tnote{**} & 90.8\tnote{**} & 93.2\tnote{*} & 81.7\tnote{**}  \\
    % SDA-Rot & 85.1 & 78.5 & 81.3 & \textbf{68.2} & 88.7 & 93.9 & \textbf{96.5} & 84.7  \\
    SSDA - Jigsaw \cite{xu2019self} & 90.0\tnote{**} & \textbf{81.2} & 79.5\tnote{**} & 64.4\tnote{**} & 88.3\tnote{**} & 94.2\tnote{**} & 95.7\tnote{*} & 84.9\tnote{**}  \\
    % SRA (ours) & \textbf{88.9}\tnote{+} & 74.1\tnote{**} & 80.1\tnote{**} & \textbf{71.1} & \textbf{98.2} & \textbf{96.6} & \textbf{96.7} & \textbf{86.6} \\
    SRA (ours) & \textbf{93.4} & 72.9\tnote{**} & \textbf{82.7}\tnote{+} & \textbf{67.9} & \textbf{96.5} & \textbf{97.0} & \textbf{97.2} & \textbf{86.9} \\
    \midrule
    Target only$^{\dagger}$ & 94.6\tnote{+} & 83.6\tnote{**} & 92.6\tnote{**} & 88.7\tnote{**} & 95.4\tnote{+} & 97.8\tnote{*} & 98.5\tnote{*} & 93.0\tnote{**} \\
    \bottomrule
    \bottomrule
    \end{tabular}
    \begin{tablenotes}
        \item[$\ddagger$] Direct transfer learning: trained on the source domain only, no adaptation (lower bound).
        \item[$\dagger$] Fully supervised: trained knowing all labels of the target domain (upper bound).
        \item[+] $\ p\geq0.05$; $^{*}\ p<0.05$; $^{**}\ p<0.001$; unpaired t-test with respect to the top result.
    \end{tablenotes}
    \end{threeparttable}
    \end{footnotesize}
\end{table*}