Multi-task learning is a branch of research where a single/few inputs are used to predict several different but ultimately connected outputs. For example, in a self-driving car, the model needs to identify obstacles, plan routes, give the right amount of throttle/brake and steering, to name but a few. It needs to do all of these in a split second by considering the same set of inputs (which would come from several sensors)
From the various use cases we have solved so far, we are in a position to train a neural network and estimate the age of a person given an image or predict the gender of the person given an image, separately, one task at a time. However, we have not looked at a scenario where we will be able to predict both age and gender in a single shot from an image. Predicting two different attributes in a single shot is important, as the same image is used for both predictions (this will be further...
