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Large pretrained models, coupled with fine-tuning, are slowly becomingestablished as the dominant architecture in machine learning. Even though thesemodels offer impressive performance, their practical application is oftenlimited by the prohibitive amount of resources required for every inference.Early-exiting dynamic neural networks (EDNN) circumvent this issue by allowinga model to make some of its predictions from intermediate layers (i.e.,early-exit). Training an EDNN architecture is challenging as it consists of twointertwined components: the gating mechanism (GM) that controls early-exitingdecisions and the intermediate inference modules (IMs) that perform inferencefrom intermediate representations. As a result, most existing approaches relyon thresholding confidence metrics for the gating mechanism and strive toimprove the underlying backbone network and the inference modules. Althoughsuccessful, this approach has two fundamental shortcomings: 1) the GMs and theIMs are decoupled during training, leading to a train-test mismatch; and 2) thethresholding gating mechanism introduces a positive bias into the predictiveprobabilities, making it difficult to readily extract uncertainty information.We propose a novel architecture that connects these two modules. This leads tosignificant performance improvements on classification datasets and enablesbetter uncertainty characterization capabilities.

Jointly-Learned Exit and Inference for a Dynamic Neural Network : JEI-DNN