# SBNN: A Searched Binary Neural Network for SAR Ship Classification
## 3 SBNN
### Searched Normal Cell and Reduction Cell
#### 3.2.2 Modification and Binarization
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Original** & **Binary** & **Original** & **Binary** & **First** & **First** & **First** \\
**Operation** & **Operation** & **Blocks(s)** & **Block(s)** & **Convolution** & **Convolution** & **Convolution** \\ \hline \multirow{4}{*}{\(3\times 3\) Conv} & & \#1 Depthwise & \#1 ReActNet & \multirow{4}{*}{\(\#1\times 3\) Conv} & \multirow{4}{*}{1} & \multirow{4}{*}{1} \\  & & Separable & Normal & & & \\  & & Convolution & Block & & & \\  & & \#2 Depthwise & \#2 ReActNet & \multirow{4}{*}{1} & \multirow{4}{*}{1} \\  & & Separable & Normal & & & \\  & & Convolution & Block & & & \\ \hline \multirow{4}{*}{\(5\times 5\) Conv} & & \#1 Depthwise & \#1 ReActNet & \multirow{4}{*}{\(\#1\times 5\) Conv} & \multirow{4}{*}{1} & \multirow{4}{*}{1} \\  & & Separable & Normal & & & \\  & & Convolution & Block & & & \\ \cline{1-1}  & & \#1 Depthwise & \#1 ReActNet & \multirow{4}{*}{\(\#1\times 5\) Conv} & \multirow{4}{*}{1} & \multirow{4}{*}{1} \\  & & Separable & Normal & & & \\  & & Convolution & Block & & & \\ \hline \multirow{4}{*}{\(3\times 3\) Div.conv} & Binary & Depthwise & ReActNet & \multirow{4}{*}{\(3\times 3\)} & \multirow{4}{*}{1} & \multirow{4}{*}{2} \\  & & \(3\times 3\) & Separable & Normal & & \\ \cline{1-1}  & Dil\_conv &
\begin{tabular}{} \end{tabular} & Convolution & Block & & \\ \hline \multirow{4}{*}{\(5\times 5\) Conv} & Binary & Depthwise & ReActNet & \multirow{4}{*}{\(5\times 5\)} & \multirow{4}{*}{1} & \multirow{4}{*}{2} \\  & \(5\times 5\) & Separable & Normal & & & \\ \cline{1-1}  & Dil\_conv & Convolution & Block & & & \\ \hline \multirow{4}{*}{Reduction} & & \#1 Depthwise & \#1 ReActNet & \multirow{4}{*}{\(\#1\times 3\)} & \multirow{4}{*}{2} & \multirow{4}{*}{1} \\  & & Separable & Reduction & Block & & \\ \cline{1-1}  & & \#2 Depthwise & \#2 ReActNet & \multirow{4}{*}{\(\#2\times 3\)} & \multirow{4}{*}{1} & \multirow{4}{*}{1} \\  & & Separable & Normal & & & \\ \cline{1-1}  & & Convolution & Block & & & \\ \hline \multirow{4}{*}{Reduction} & & \#1 Depthwise & \#1 ReActNet & \multirow{4}{*}{\(\#1\times 5\)} & \multirow{4}{*}{2} & \multirow{4}{*}{1} \\  & & Separable & Reduction & Block & & \\ \cline{1-1}  & & Convolution & Block & & & \\ \cline{1-1}  & & \#2 Depthwise & \#2 ReActNet & \multirow{4}{*}{\(\#2\times 5\)} & \multirow{4}{*}{1} & \multirow{4}{*}{1} \\  & & Separable & Convolution & Block & & \\ \cline{1-1}  & & Convolution & Block & & & \\ \hline \multirow{4}{*}{Reduction} & Binary & \multirow{4}{*}{\(3\times 3\) Div.conv} & \multirow{4}{*}{\(3\times 3\) Div.conv} & \multirow{4}{*}{\(3\times 3\) Div.conv} & \multirow{4}{*}{1} & \multirow{4}{*}{1} \\  & & Dil\_conv & Convolution & Block & & \\ \hline \multirow{4}{*}{Reduction} & Binary & \multirow{4}{*}{\(3\times 3\) Div.conv} & \multirow{4}{*}{\(3\times 3\) Div.conv} & \multirow{4}{*}{1} & \multirow{4}{*}{1} \\  & & Separable & Reduction & Block & & \\ \cline{1-1}  & & Separable & Reduction & Block & & \\ \cline{1-1}  & & Convolution & Block & & & \\ \hline \multirow{4}{*}{Reduction} & Binary & \multirow{4}{*}{\(3\times 3\) Div.conv} & \multirow{4}{*}{1} & \multirow{4}{*}{1} & \multirow{4}{*}{1} \\  & & Separable & Reduction & Block & & \\ \cline{1-1}  & & Separable & Reduction & Block & & \\ \cline{1-1}  & & Convolution & Block & & & \\ \hline \multirow{4}{*}{Reduction} & Binary & \multirow{4}{*}{\(3\times 3\) Div.conv} & \multirow{4}{*}{1} & \multirow{4}{*}{1} & \multirow{4}{*}{1} \\  & & Separable & Reduction & Block & & \\ \cline{1-1}  & & Separable & Reduction & Block & & \\ \cline{1-1}  & & Convolution & Block & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The details of the modification and binarization of SBNN. In ReActNet Blocks, floating-point feature maps are transformed into binary feature maps by ReAct Sign function. The traditional Sign function may harm the training due to an unsuitable truncation threshold. When the threshold is low, lots of background noise makes the binarized feature maps become unclear.