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We propose a novel variant of SGD customized for training networkarchitectures that support anytime behavior: such networks produce a series ofincreasingly accurate outputs over time. Efficient architectural designs forthese networks focus on re-using internal state; subnetworks must producerepresentations relevant for both immediate prediction as well as refinement bysubsequent network stages. We consider traditional branched networks as well asa new class of recursively nested networks. Our new optimizer, OrthogonalizedSGD, dynamically re-balances task-specific gradients when training a multitasknetwork. In the context of anytime architectures, this optimizer projectsgradients from later outputs onto a parameter subspace that does not interferewith those from earlier outputs. Experiments demonstrate that training withOrthogonalized SGD significantly improves generalization accuracy of anytimenetworks.

Orthogonalized SGD and Nested Architectures for Anytime Neural Networks