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End-to-End training (E2E) is becoming more and more popular to train complexDeep Network architectures. An interesting question is whether this trend willcontinue-are there any clear failure cases for E2E training? We study thisquestion in depth, for the specific case of E2E training an ensemble ofnetworks. Our strategy is to blend the gradient smoothly in between twoextremes: from independent training of the networks, up to to full E2Etraining. We find clear failure cases, where over-parameterized models cannotbe trained E2E. A surprising result is that the optimum can sometimes lie inbetween the two, neither an ensemble or an E2E system. The work also uncoverslinks to Dropout, and raises questions around the nature of ensemble diversityand multi-branch networks.

To Ensemble or Not Ensemble: When does End-To-End Training Fail?