you maybe have a sample of the green class here it will push the network such that the representation of that sample will go on to the other side of this decision boundary and it will push the decision boundary at the same time to make that happen more easily right so it will optimize all of this at the same time that's what you do that's how you optimize representations so this work here and another work has said wouldn't it be great if the representation and decision boundaries weren't just trained at the same time for this but we learn