How to Train Your Multi-Exit Model? Analyzing the Impact of Training Strategies

Early exits enable the network's forward pass to terminate early by attaching trainable internal classifiers to the backbone network. Existing early-exit methods typically adopt either a joint training approach, where the backbone and exit heads are trained simultaneously, or a disjoint approach, where the heads are trained separately. However, the implications of this choice are often overlooked, with studies typically adopting one approach without adequate justification. This choice influences training dynamics and its impact remains largely unexplored. In this paper, we introduce a set of metrics to analyze early-exit training dynamics and guide the choice of training strategy. We demonstrate that conventionally used joint and disjoint regimes yield suboptimal performance. To address these limitations, we propose a mixed training strategy: the backbone is trained first, followed by the training of the entire multiexit network. Through comprehensive evaluations of training strategies across various architectures, datasets, and early-exit methods, we present the strengths and weaknesses of the early exit training strategies. In particular, we show consistent improvements in performance and efficiency using the proposed mixed strategy. How to Train Your Multi-Exit Model? Analyzing the Impact of Training Strategies network first, then freeze its weights and train the parameters of the newly added ICs in the second, separate phase of training (Teerapittayanon et al., 2016; Liao et al., 2021; Zhou et al., 2020) ("disjoint" regime). To the best of our knowledge, no study compares or explores the relationship between these training regimes.