让AI模型对简单任务快速响应，复杂任务再深度处理。
A Survey of Early Exit Deep Neural Networks in NLP
- 在深层网络中插入多层分类器，根据难度提前退出
- 简单样本可仅用浅层完成推理，速度提升显著
- 适合移动端、实时系统等资源受限场景
深度神经网络（DNN）为实现多项任务的顶尖性能而不断增大规模，但其高计算需求使其难以应用于资源受限场景。真实数据集常混合简单与复杂样本，需具备自适应推理能力。早期退出策略通过在不同层添加分类器，使简单样本可由浅层快速分类，从而加速整体推理过程。该方法不仅降低延迟，还增强模型对对抗攻击的鲁棒性。本文全面综述了早期退出方法及其在自然语言处理中的应用。
原文摘要 · Abstract (English)
Deep Neural Networks (DNNs) have grown increasingly large in size to achieve state of the art performance across a wide range of tasks. However, their high computational requirements make them less suitable for resource-constrained applications. Also, real-world datasets often consist of a mixture of easy and complex samples, necessitating adaptive inference mechanisms that account for sample difficulty. Early exit strategies offer a promising solution by enabling adaptive inference, where simpler samples are classified using the initial layers of the DNN, thereby accelerating the overall inference process. By attaching classifiers at different layers, early exit methods not only reduce inference latency but also improve the model robustness against adversarial attacks. This paper presents a comprehensive survey of early exit methods and their applications in NLP.
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