面向知识蒸馏的学生导向教师知识精炼
Student-Oriented Teacher Knowledge Refinement for Knowledge Distillation
AI总结：
针对师生模型差异导致知识迁移效果不佳的问题，提出SoKD方法，通过可学习特征增强策略动态精炼教师知识，并利用DAM模块聚焦共同感兴趣区域，有效提升了知识蒸馏的性能与泛化能力。
摘要：
知识蒸馏因其能够将大型教师网络的知识迁移到紧凑且更精简的学生网络中而得到广泛认可。传统的知识蒸馏方法主要遵循面向教师的范式，将学习教师复杂知识的任务强加给学生网络。然而，模型容量和架构设计上的显著差异阻碍了学生对教师传授的复杂知识的理解，导致性能次优。本文引入了一种强调以学生为导向的新颖视角，并精炼教师的知识以更好地契合学生的需求，从而提升知识迁移的有效性。具体而言，我们提出了面向学生的知识蒸馏（SoKD），在训练过程中引入可学习的特征增强策略，以动态地精炼教师对学生的知识。此外，我们部署了独特区域检测模块（DAM）来识别教师和学生之间共同感兴趣的区域，将知识迁移集中在这些关键区域内，以避免迁移无关信息。这种定制化模块确保了更加聚焦且有效的知识蒸馏过程。我们的方法作为插件，可以与各种知识蒸馏方法相集成。大量实验结果证明了我们方法的有效性和泛化能力。
英文摘要：
Knowledge distillation has become widely recognized for its ability to transfer knowledge from a large teacher network to a compact and more streamlined student network. Traditional knowledge distillation methods primarily follow a teacher-oriented paradigm that imposes the task of learning the teacher's complex knowledge onto the student network. However, significant disparities in model capacity and architectural design hinder the student's comprehension of the complex knowledge imparted by the teacher, resulting in sub-optimal performance. This paper introduces a novel perspective emphasizing student-oriented and refining the teacher's knowledge to better align with the student's needs, thereby improving knowledge transfer effectiveness. Specifically, we present the Student-Oriented Knowledge Distillation (SoKD), which incorporates a learnable feature augmentation strategy during training to refine the teacher's knowledge of the student dynamically. Furthermore, we deploy the Distinctive Area Detection Module (DAM) to identify areas of mutual interest between the teacher and student, concentrating knowledge transfer within these critical areas to avoid transferring irrelevant information. This customized module ensures a more focused and effective knowledge distillation process. Our approach, functioning as a plug-in, could be integrated with various knowledge distillation methods. Extensive experimental results demonstrate the efficacy and generalizability of our method.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
