论文标题
转移类间相关性
Transferring Inter-Class Correlation
论文作者
论文摘要
在分类任务中广泛使用了教师（T-S）框架，通过该任务可以通过从另一个受过训练的神经网络（教师）转移知识来改善一个神经网络（学生）的表现。由于转移知识与教师和学生之间的网络能力和结构有关，因此如何定义有效的知识仍然是一个悬而未决的问题。为了解决这个问题，我们设计了一种新颖的传递知识，在输出层中基于自我注意的阶层间相关（ICC）图，并提出了我们的T-S框架，阶层间相关转移（ICCT）。
The Teacher-Student (T-S) framework is widely utilized in the classification tasks, through which the performance of one neural network (the student) can be improved by transferring knowledge from another trained neural network (the teacher). Since the transferring knowledge is related to the network capacities and structures between the teacher and the student, how to define efficient knowledge remains an open question. To address this issue, we design a novel transferring knowledge, the Self-Attention based Inter-Class Correlation (ICC) map in the output layer, and propose our T-S framework, Inter-Class Correlation Transfer (ICCT).
