  Deep learning classifiers are assisting humans in making decisions and hence
the user's trust in these models is of paramount importance. Trust is often a
function of constant behavior. From an AI model perspective it means given the
same input the user would expect the same output, especially for correct
outputs, or in other words consistently correct outputs. This paper studies a
model behavior in the context of periodic retraining of deployed models where
the outputs from successive generations of the models might not agree on the
correct labels assigned to the same input. We formally define consistency and
correct-consistency of a learning model. We prove that consistency and
correct-consistency of an ensemble learner is not less than the average
consistency and correct-consistency of individual learners and
correct-consistency can be improved with a probability by combining learners
with accuracy not less than the average accuracy of ensemble component
learners. To validate the theory using three datasets and two state-of-the-art
deep learning classifiers we also propose an efficient dynamic snapshot
ensemble method and demonstrate its value.
