The increasing complexity of advanced machine learning models requires
innovative approaches to manage computational resources effectively. One such
method is the Early Exit strategy, which allows for adaptive computation by
providing a mechanism to shorten the processing path for simpler data
instances. In this paper, we propose EERO, a new methodology to translate the
problem of early exiting to a problem of using multiple classifiers with reject
option in order to better select the exiting head for each instance. We
calibrate the probabilities of exiting at the different heads using aggregation
with exponential weights to guarantee a fixed budget .We consider factors such
as Bayesian risk, budget constraints, and head-specific budget consumption.
Experimental results, conducted using a ResNet-18 model and a ConvNext
architecture on Cifar and ImageNet datasets, demonstrate that our method not
only effectively manages budget allocation but also enhances accuracy in
overthinking scenarios.

本研究提出了一种名为 EERO 的新方法，将早期退出的问题转化为使用具有拒绝选项的多个分类器的问题，以更好地选择每个实例的退出头。实验证明，该方法不仅有效管理预算分配，而且在超思考场景中提高了准确性。