  A new understanding of adversarial examples and adversarial robustness is
proposed by decoupling the data generator and the label generator (which we
call the teacher). In our framework, adversarial robustness is a conditional
concept---the student model is not absolutely robust, but robust with respect
to the teacher. Based on the new understanding, we claim that adversarial
examples exist because the student cannot obtain sufficient information of the
teacher from the training data. Various ways of achieving robustness is
compared. Theoretical and numerical evidence shows that to efficiently attain
robustness, a teacher that actively provides its information to the student may
be necessary.
