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Advanced Convolutional Neural Networks
In this section, we will look at the computer vision tasks in the following diagram
and show how they can be solved by composing CNNs into larger and more
complex architectures:
Figure 1: Different computer vision tasks. Source: Introduction to Artificial Intelligence and Computer
Vision Revolution (https://www.slideshare.net/darian_f/introduction-to-the-artificial-intelligence-andcomputer-vision-revolution).
Classification and localization
In the classification and localization task not only do you have to report the class of
object found in the image, but also the coordinates of the bounding box where the
object appears in the image. This type of task assumes that there is only one instance
of the object in an image.
This can be achieved by attaching a "regression head" in addition to the
"classification head" in a typical classification network. Recall that in a classification
network, the final output of convolution and pooling operations, called the
feature map, is fed into a fully connected network that produces a vector of class
probabilities. This fully connected network is called the classification head, and it is
tuned using a categorical loss function (L c
) such as categorical cross entropy.
Similarly, a regression head is another fully connected network that takes the feature
map and produces a vector (x, y, w, h) representing the top-left x and y coordinates,
width and height of the bounding box. It is tuned using a continuous loss function
(L r
) such as mean squared error. The entire network is tuned using a linear
combination of the two losses, that is:
LL = ααLL CC + (1 − αα)LL rr
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