pdfcoffee

Chapter 5
Here αα is a hyperparameter and can take a value between 0 and 1. Unless the value
is determined by some domain knowledge about the problem, it can be set to 0.5.
The following figure shows a typical classification and localization network
architecture. As you can see, the only difference with respect to a typical CNN
classification network is the additional regression head on the top right:
Figure 2: Network architecture for image classification and localization
Semantic segmentation
Another class of problem that builds on the basic classification idea is "semantic
segmentation." Here the aim is to classify every single pixel on the image as
belonging to a single class.
An initial method of implementation could be to build a classifier network for each
pixel, where the input is a small neighborhood around each pixel. In practice, this
approach is not very performant, so an improvement over this implementation
might be to run the image through convolutions that will increase the feature depth,
while keeping the image width and height constant. Each pixel then has a feature
map that can be sent through a fully connected network that predicts the class of the
pixel. However, in practice, this is also quite expensive, and it is not normally used.
A third approach is to use a CNN encoder-decoder network, where the encoder
decreases the width and height of the image but increases its depth (number of
features), while the decoder uses transposed convolution operations to increase its
size and decrease depth. Transpose convolution (or upsampling) is the process of
going in the opposite direction of a normal convolution. The input to this network is
the image and the output is the segmentation map.
[ 141 ]