摘要
Domain adaptation and adversarial networks are two main approaches for transfer learning.Domain adaptation methods match the mean values of source and target domains,which requires a very large batch size during training.However,adversarial networks are usually unstable when training.In this paper,we propose a joint method of feature matching and adversarial networks to reduce domain discrepancy and mine domaininvariant features from the local and global aspects.At the same time,our method improves the stability of training.Moreover,the method is embedded into a unified convolutional neural network that can be easily optimized by gradient descent.Experimental results show that our joint method can yield the state-of-the-art results on three common public datasets.
Domain adaptation and adversarial networks are two main approaches for transfer learning. Domain adaptation methods match the mean values of source and target domains, which requires a very large batch size during training. However, adversarial networks are usually unstable when training. In this paper, we propose a joint method of feature matching and adversarial networks to reduce domain discrepancy and mine domaininvariant features from the local and global aspects. At the same time, our method improves the stability of training. Moreover, the method is embedded into a unified convolutional neural network that can be easily optimized by gradient descent. Experimental results show that our joint method can yield the state-of-the-art results on three common public datasets.
作者
钟昊文
王超
庹红娅
胡健
乔凌峰
敬忠良
ZHONG Haowen;WANG Chao;TUO Hongya;HU Jian;QIAO Lingfeng;JING Zhongliang(School of Aeronautics and Astronautics,Shanghai Jiao Tong University,Shanghai 200240,China)
基金
the Aerospace Science and Technology Foundation(No.20115557007)
the National Natural Science Foundation of China(No.61673262)
the Military Science and Technology Foundation of China(No.18-H863-03-ZT-001-006-06)
