Hi,
Answering for the title, the activation function extracts the features of a trained network. For example, lets take pretrained VGG networks and your image dataset the activations function returns the features set from the images using the pretrained VGG. The features can be used for different utilities like transfer learning etc.,.
Coming to your question in the question section, it really depends on what you meant by the result from the total image is better ? activations function at the end returns the features extracted from the dataset that is passed to the network(pretrained) in single go.
For better understanding, the example in the link below explains in deeper
Hope this helps!
