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Table B.1.
Network architecture used in our experiments.
| Layer | # filters | Function |
|---|---|---|
| Conv2D+ReLU 1 | n | Conv 3 × 3 + ReLU |
| Conv2D+ReLU 2 | n | Conv 3 × 3 + ReLU |
| MaxPooling 1 | n | Maxpooling 2 × 2 |
| Conv2D+ReLU 3 | n × 2 | Conv 3 × 3 + ReLU |
| Conv2D+ReLU 4 | n × 2 | Conv 3 × 3 + ReLU |
| MaxPooling 2 | n × 2 | MaxPooling 2 × 2 |
| Conv2D+ReLU 5 | n × 4 | Conv 3 × 3 + ReLU |
| Conv2D+ReLU 6 | n × 4 | Conv 3 × 3 + ReLU |
| Upsampling 1 | n × 4 | Upsampling 2 × 2 |
| Conv2D+ReLU 7 | n × 2 | Conv 3 × 3 + ReLU |
| Concatenate 1 | n × 2 | Concat with Conv2D+ReLU 4 |
| Conv2D+ReLU 8 | n × 2 | Conv 3 × 3 + ReLU |
| Conv2D+ReLU 9 | n × 2 | Conv 3 × 3 + ReLU |
| Upsampling 2 | n × 2 | Upsampling 2 × 2 |
| Conv2D+ReLU 10 | n | Conv 3 × 3 + ReLU |
| Concatenate 1 | n | Concat with Conv2D+ReLU 2 |
| Conv2D+ReLU 11 | n | Conv 3 × 3 + ReLU |
| Conv2D+ReLU 12 | n | Conv 3 × 3 + ReLU |
| Network Output | m | Conv 1 × 1 |
Notes. Number of network input feature maps n and the dimension of the output m depend on the experiment.
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