Table 5.

Details of the generator network

Block	Input	Conv 1-4	Conv 5	Conv 6-9	Output
Output Size	(640, 640, 3)	$\left(\frac{640}{2^{n-1}},\frac{640}{2^{n-1}},32\ast 2^{n-1}\right)$	$\left(\frac{640}{2^{n-1}},\frac{640}{2^{n-1}},32\ast 2^{n-1}\right)$	$\left(\frac{640}{2^{9-n}},\frac{640}{2^{9-n}},32\ast 2^{9-n}\right)$	(640, 640, 1)
Upsample	-	-	-	Conv n − 1	-
Depth Concat	-	-	-	Conv 10 − n	-
Operations	-	$\begin{array}{l}\left\{\begin{array}{l}3\times 3\text{conv}\\ \text{batch-norm}\\ \text{ReLU}\end{array}\right\}\times 2\\ 2\times 2\text{maxpool}\end{array}$	$\left\{\begin{array}{l}3\times 3\text{conv}\\ \text{batch-norm}\\ \text{ReLU}\end{array}\right\}\times 2$	$\left\{\begin{array}{l}3\times 3\text{conv}\\ \text{batch-norm}\\ \text{ReLU}\end{array}\right\}\times 2$	$\left\{\begin{array}{l}1\times 1\text{conv}\\ \text{sigmoid}\end{array}\right\}\times 1$

Output size represents (width, height, depth). n refers to index of convolution block from 1 to 9. In conv 6-9, lower layer and upsampled layer are first concatenated along depth and is applied operations designated. Output depth for convolution operations is set equal to output depth of the block