Transfer learning for application of discriminators to other regions

Abstract

Monitoring road and transportation conditions in real-time using edge computing is conducted to achieve more advanced road and transportation management. In order to accurately monitor various road and transportation conditions, it is necessary to re-learn discriminators that analyze data sequentially at each distributed edge. In order to efficiently re-learn discriminators, decentralized learning, in which edges directly exchange data with each other, is desirable. In this paper, we propose a method to re-learn a discriminator based on a neural network using the knowledge possessed by other edges. In the proposed method, a part of the weight matrices of other discriminators is transferred to its own discriminator, and the process of adapting the replaced weight matrices to its own discriminator is repeated using the error backpropagation method. By repeatedly replacing the weight matrices and applying the error backpropagation method, stable re-training of discriminators can be expected.