One of the backbone assumptions underpinning the generalization theory of supervised learning algorithms is that the test distribution should be the same as the training distribution. However in many real-world applications it is usually time-consuming or even infeasible to collect labeled data from all the possible scenarios where our learning system is going to be deployed. For example, consider a typical application of vehicle counting, where we would like to count how many cars are there in a given image…
