Knowledge Distillation Under Ideal Joint Classifier Assumption
Knowledge Distillation Under Ideal Joint Classifier Assumption
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DOI：
10.1016/j.neunet.2024.106160
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发表时间：
2023-04
期刊：
影响因子：
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通讯作者：
Huayu Li;Xiwen Chen;G. Ditzler;Ping Chang;Janet Roveda;Ao Li
中科院分区：
文献类型：
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作者：
Huayu Li;Xiwen Chen;G. Ditzler;Ping Chang;Janet Roveda;Ao Li
知识蒸馏是一种有效的方法，可以将大量的神经网络压缩成更紧凑、更高效的对应网络。在这种情况下，softmax回归表示学习作为一种广泛接受的方法，利用预先建立的教师网络来指导小型学生网络的学习过程。值得注意的是，尽管对softmax回归表示学习的有效性进行了广泛的研究，但管理知识转移机制的复杂基础仍然没有得到充分的阐明。本研究介绍了“理想的联合分类器知识蒸馏”（IJCKD）框架，一个总体的范式，不仅清晰和详尽的理解，流行的知识蒸馏技术，但也建立了一个理论基础的前瞻性调查。运用领域适应理论中的数学方法，对学生网络依赖于教师网络的误差边界进行了全面的考察。因此，我们的框架促进了教师和学生网络之间的高效知识转移，从而适应各种应用。
Knowledge distillation constitutes a potent methodology for condensing substantial neural networks into more compact and efficient counterparts. Within this context, softmax regression representation learning serves as a widely embraced approach, leveraging a pre-established teacher network to guide the learning process of a diminutive student network. Notably, despite the extensive inquiry into the efficacy of softmax regression representation learning, the intricate underpinnings governing the knowledge transfer mechanism remain inadequately elucidated. This study introduces the ‘Ideal Joint Classifier Knowledge Distillation’ (IJCKD) framework, an overarching paradigm that not only furnishes a lucid and exhaustive comprehension of prevailing knowledge distillation techniques but also establishes a theoretical underpinning for prospective investigations. Employing mathematical methodologies derived from domain adaptation theory, this investigation conducts a comprehensive examination of the error boundary of the student network contingent upon the teacher network. Consequently, our framework facilitates efficient knowledge transference between teacher and student networks, thereby accommodating a diverse spectrum of applications.