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<p>In recent years, dynamic early-exiting neural networks have emerged as an efficient solution for balancing classification performance and inference cost–typically measured in FLOPs–in image classification tasks. Given the significance of this domain, numerous extensions to early-exiting networks have been proposed over the past five years, focusing on improving this trade-off. This paper critically analyzes these advancements and, through extensive experimentation, demonstrates that a simple yet carefully designed architecture–eschewing unnecessary complexities–can achieve results that surpass the current state-of-the-art models when paired with appropriate algorithmic strategies. We introduce SEEDNet (Simple Early-Exiting Dynamic Image Network), a streamlined early-exiting dynamic image network that leverages a combination of mechanisms to enhance both efficiency and efficacy. By integrating early-exiting strategies within a dynamic framework, SEEDNet achieves a top-1 accuracy of <InlineEquation ID="IEq1"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="138_2025_1748_Article_IEq1.gif" Format="GIF" Height="16" Rendition="HTML" Resolution="72" Type="Linedraw" Width="114" /> </InlineMediaObject> <EquationSource Format="TEX">\(81.22\% \pm 0.11\%\)</EquationSource> </InlineEquation> on ImageNet while requiring only <InlineEquation ID="IEq2"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="138_2025_1748_Article_IEq2.gif" Format="GIF" Height="17" Rendition="HTML" Resolution="72" Type="Linedraw" Width="52" /> </InlineMediaObject> <EquationSource Format="TEX">\(2 \times 10^9\)</EquationSource> </InlineEquation> FLOPs during inference. This demonstrates that the right existing mechanisms have been selected and integrated to obtain a more efficient architecture. To promote transparency and encourage further advancements in the field, we will release our code to the community.</p>

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Toward simplicity in dynamic inference: a critical study and redesign of early-exit networks

  • 期刊论文

  • 发表日期：

    2025年10月11日

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  • Youva Addad,
  • Alexis Lechervy,
  • Frederic Jurie

摘要

In recent years, dynamic early-exiting neural networks have emerged as an efficient solution for balancing classification performance and inference cost–typically measured in FLOPs–in image classification tasks. Given the significance of this domain, numerous extensions to early-exiting networks have been proposed over the past five years, focusing on improving this trade-off. This paper critically analyzes these advancements and, through extensive experimentation, demonstrates that a simple yet carefully designed architecture–eschewing unnecessary complexities–can achieve results that surpass the current state-of-the-art models when paired with appropriate algorithmic strategies. We introduce SEEDNet (Simple Early-Exiting Dynamic Image Network), a streamlined early-exiting dynamic image network that leverages a combination of mechanisms to enhance both efficiency and efficacy. By integrating early-exiting strategies within a dynamic framework, SEEDNet achieves a top-1 accuracy of \(81.22\% \pm 0.11\%\) on ImageNet while requiring only \(2 \times 10^9\) FLOPs during inference. This demonstrates that the right existing mechanisms have been selected and integrated to obtain a more efficient architecture. To promote transparency and encourage further advancements in the field, we will release our code to the community.