Class Means as an Early Exit Decision Mechanism
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State-of-the-art neural networks with early exit mechanisms often need considerable amount of training and fine-tuning to achieve good performance with low computational cost. We propose a novel early exit technique based on the class means of samples. Unlike most existing schemes, our method does not require gradient-based training of internal classifiers. This makes our method particularly useful for neural network training in low-power devices, as in wireless edge networks. In particular, given a fixed training time budget, our scheme achieves higher accuracy as compared to existing early exit mechanisms. Moreover, if there are no limitations on the training time budget, our method can be combined with an existing early exit scheme to boost its performance, achieving a better trade-off between computational cost and network accuracy.
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