Lets say I am creating a fully convolutional classifier for identifying different types of animals. Now lets assume that I know what part of the animal kingdom the animal belongs to beforehand (mammal, reptile, amphibian .. etc). How would I present this extra information to the fully convolutional network?
Any ideas or research would be very helpful.
Edit* More specifically I am concerned with domain adaptation as shown here, In this example I am using a fully convolutional network without any pooling or striding, where the output is the same size as the input (i.e. an image transformed from one domain to another). In this respect I want to be able run the domain adaptation on images of any size. Adding extra channels to the input seems to make the most sense, however if I am one-hot encoding a bunch of categorical variables, this will consume a lot of memory.