data/tables/dev/val_tab_0072.tex

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\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth}
  \begin{tabular}{rcccccc}
    \toprule
    Method    &  \multicolumn{2}{c}{Parameters ($\downarrow$)} & \multicolumn{2}{c}{Inference time (ms) ($\downarrow$)} & \multicolumn{2}{c}{Mult-adds (B) ($\downarrow$)} \\
    \midrule
      Backbone & VGG-11  & ResNet-18  & VGG-11  & ResNet-18 & VGG-11 & ResNet-18  \\
    \midrule
    MC-Dropout      & 0.0\%   & 0.0\%  & 16$\times$ 2.5 & 16$\times$2.4 & 16$\times$0.15 & 16$\times$0.56 \\
    Deep Ensemble   & 1500\%    & 1500\%  & 16$\times$ 2.3 & 16$\times$ 1.8 & 16$\times$0.15 & 16$\times$0.56 \\
    \midrule

    Masksemble  & 0.0\%  & 0.0\%  & 20.8  & 6.6 & 2.45 & 8.89  \\
    MIMO            & 1.1\%  & 0.9\%  & 2.7  & 1.9 & 0.18 & 0.58 \\
    BatchEnsemble  & 1.4\%   & 5.2\%  & 2.8  & 5.2 & 2.44 & 8.89 \\
    FiLM-Ensemble  & 0.9\%   & 1.3\%  & 2.8 & 5.7 & 2.45 & 8.89 \\
    \bottomrule
  \end{tabular}
\end{adjustbox}
\caption{Table 1: Memory and inference complexity comparison (CIFAR-10/100 datasets): Number of additional trainable parameters to have 16 ensemble members for different backbones. The inference time (mult-adds) shown corresponds to the mean GPU time (number of multiply-add operations) required to run a forward pass for a batch of size 1 with 16 ensemble members. The bottom section comprises methods whose forward and backward passes are implemented in parallel over ensemble members. Measurements are done on an NVIDIA GeForce GTX 1080 Ti.}
\end{table}
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