# CLEANIR: Controllable Attribute-Preserving Natural Identity Remover
## 3 Proposed Method
### Network Architecture
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Name** & **Operations** & **Input** & **Output Size** \\ \hline Enc0 & 7x7 Conv(stride=2)-BN-LReLU & Input image & 32 \(\times\) 32 \(\times\) 64 \\ \hline EncBlock\_1\_1 & 3x3 Conv(stride=2)-BN-LReLU & Enc0 & \(16\times 16\times 64\) \\ \hline EncBlock\_2 & 3x3 Conv(stride=2)-BN & Enc0 & \(16\times 16\times 64\) \\ \hline EncBlock\_3 & Add-LReLU & EncBlock1\_1, EncBlock1\_2 & \(16\times 16\times 64\) \\ \hline EncBlock\_4 & { 3x3 Conv(stride=1)-BN-LReLU } x2 & EncBlock1\_3 & \(16\times 16\times 64\) \\ \hline EncBlock\_5 & Add-LReLU & EncBlock1\_3, EncBlock1\_4 & \(16\times 16\times 64\) \\ \hline EncBlock\_1 & 3x3 Conv(stride=2)-BN-LReLU- & EncBlock1\_5 & \(8\times 8\times 128\) \\ \hline EncBlock\_2 & 3x3 Conv(stride=2)-BN & EncBlock1\_5 & \(8\times 8\times 128\) \\ \hline EncBlock\_3 & Add-LReLU & EncBlock2\_1, EncBlock2\_2 & \(8\times 8\times 128\) \\ \hline EncBlock\_4 & { 3x3 Conv(stride=1)-BN-LReLU } x2 & EncBlock2\_3 & \(8\times 8\times 128\) \\ \hline EncBlock\_5 & Add-LReLU & EncBlock2\_3, EncBlock2\_4 & \(8\times 8\times 128\) \\ \hline EncBlock\_1 & 3x3 Conv(stride=2)-BN-LReLU & EncBlock2\_5 & \(4\times 4\times 192\) \\ \hline EncBlock\_2 & 3x3 Conv(stride=2)-BN & EncBlock2\_5 & \(4\times 4\times 192\) \\ \hline EncBlock\_3 & Add-LReLU & EncBlock3\_1, EncBlock3\_2 & \(4\times 4\times 192\) \\ \hline EncBlock\_4 & { 3x3 Conv(stride=1)-BN-LReLU } x2 & EncBlock3\_3 & \(4\times 4\times 192\) \\ \hline EncBlock\_5 & Add-LReLU & EncBlock3\_3, EncBlock3\_4 & \(4\times 4\times 192\) \\ \hline EncBlock\_1 & 3x3 Conv(stride=2)-BN-LReLU & EncBlock3\_5 & \(2\times 2\times 256\) \\ \hline EncBlock\_2 & 3x3 Conv(stride=2)-BN & EncBlock3\_5 & \(2\times 2\times 256\) \\ \hline EncBlock\_3 & Add-LReLU & EncBlock4\_1, EncBlock4\_2 & \(2\times 2\times 256\) \\ \hline EncBlock\_4 & { 3x3 Conv(stride=1)-BN-LReLU } x2 & EncBlock4\_3 & \(2\times 2\times 256\) \\ \hline EncBlock\_5 & Add-LReLU & EncBlock4\_3, EncBlock4\_4 & \(2\times 2\times 256\) \\ \hline EncL & 3x3 AvgPool-FC-LReLU-BN & EncBlock4\_5 & \(1\times 1024\) \\ \hline Dec0 & FC-Reshape-LReLU & EncL & \(4\times 4\times 512\) \\ \hline DecBlock\_1 &
\begin{tabular}{l} Upsample- \\ {3x3 Conv(stride=1)-BN-LReLU } x2 \\ \end{tabular} & Dec0 & \(8\times 8\times 256\) \\ \hline DecBlock\_2 & Upsample-3x3 Conv(stride=1)-BN & Dec0 & \(8\times 8\times 256\) \\ \hline DecBlock\_3 & Add-LReLU & DecBlock1\_1, DecBlock1\_2 & \(8\times 8\times 256\) \\ \hline DecBlock\_4 & { 3x3 Conv(stride=1)-BN-LReLU } x2 & DecBlock1\_3 & \(8\times 8\times 256\) \\ \hline DecBlock\_5 & Add-LReLU & DecBlock1\_3, DecBlock1\_4 & \(8\times 8\times 256\) \\ \hline DecBlock\_1 & Upsample- & DecBlock1\_5 & \(16\times 16\times 128\) \\ \hline DecBlock\_2 & Upsample-3x3 Conv(stride=1)-BN & DecBlock1\_5 & \(16\times 16\times 128\) \\ \hline DecBlock\_3 & Add-LReLU & DecBlock2\_1, DecBlock2\_2 & \(16\times 16\times 128\) \\ \hline DecBlock\_2 & { 3x3 Conv(stride=1)-BN-LReLU } x2 & DecBlock2\_3 & \(16\times 16\times 128\) \\ \hline DecBlock\_5 & Add-LReLU & DecBlock2\_3, DecBlock2\_4 & \(16\times 16\times 128\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Architecture of the proposed network. Where LReLU is leaky ReLU, BN is batch normalization, FC is fully connected layer, ’3x3 Conv’ is a convolution of which filter size is 3 by 3, and, ’3x3 AvgPool’ is an average pooling of which filter size is 3 by 3.