Deep-Learning-with-PyTorch

Semantic segmentation: Per-pixel classification
363
6x6 Input
3 x3 Conv
4x4 Output
4x4 Input
(same as output)
3
x3 Conv
22
x x2
22
2x
Outputt
Max POol
1x1 Output
Figure 13.5 The convolutional architecture of a LunaModel block, consisting of two 3 × 3
convolutions followed by a max pool. The final pixel has a 6 × 6 receptive field.
(bad) due to the limited reach based on how much overlap multiple layers of small
convolutions will have. The classification model uses each downsampling layer to double
the effective reach of the following convolutions; and without that increase in
effective field size, each segmented pixel will only be able to consider a very local
neighborhood.
NOTE Assuming 3 × 3 convolutions, the receptive field size for a simple
model of stacked convolutions is 2 * L + 1, with L being the number of convolutional
layers.
Four layers of 3 × 3 convolutions will have a receptive field of 9 × 9 per output pixel. By
inserting a 2 × 2 max pool between the second and third convolutions, and another at
the end, we increase the receptive field to …
NOTE See if you can figure out the math yourself; when you’re done, check
back here.
... 16 × 16. The final series of conv-conv-pool has a receptive field of 6 × 6, but that
happens after the first max pool, which makes the final effective receptive field 12 × 12
in the original input resolution. The first two conv layers add a total border of 2 pixels
around the 12 × 12, for a total of 16 × 16.
So the question remains: how can we improve the receptive field of an output pixel
while maintaining a 1:1 ratio of input pixels to output pixels? One common answer is