Deep-Learning-with-PyTorch

Semantic segmentation: Per-pixel classification
365
UNET Architecture
skip coNnections
upsampling
ClaSsificationication
magifying
glaSs
could be fed into linear layer
Figure 13.7 From the U-Net paper, with annotations. Source: The base of this figure is courtesy Olaf Ronneberger
et al., from the paper “U-Net: Convolutional Networks for Biomedical Image Segmentation,” which can be found at
https://arxiv.org/abs/1505.04597 and https://lmb.informatik.uni-freiburg.de/people/ronneber/u-net.
resolutions at which the network operates. In the top row is the full resolution (512 ×
512 for us), the row below has half that, and so on. The data flows from top left to bottom
center through a series of convolutions and downscaling, as we saw in the classifiers
and looked at in detail in chapter 8. Then we go up again, using upscaling
convolutions to get back to the full resolution. Unlike the original U-Net, we will be
padding things so we don’t lose pixels off the edges, so our resolution is the same on
the left and on the right.
Earlier network designs already had this U-shape, which people attempted to use
to address the limited receptive field size of fully convolutional networks. To address
this limited field size, they used a design that copied, inverted, and appended the
focusing portions of an image-classification network to create a symmetrical model
that goes from fine detail to wide receptive field and back to fine detail.
Those earlier network designs had problems converging, however, most likely due
to the loss of spatial information during downsampling. Once information reaches a
large number of very downscaled images, the exact location of object boundaries gets