关键词: 生成对抗网络, 迁移学习, 梯度消失, 领域不变特征, 最小二乘生成对抗网络损失函数

Abstract: The existing Generative Adversarial Networks（GAN） loss function has been successfully used in transfer learning method. However, it is found that this loss function may lead to the vanishing gradients problem during the learning process. To overcome this problem, a new learning domain invariant feature approach, Least Squares Transfer Generative Adversarial Networks（LSTGAN）, is proposed. LSTGAN adopts least squares generative adversarial networks loss function to reduce the discrepancy of domain distribution by a single-domain discrimination training way. The research shows that the proposed method has certain advantages compared with other competitive algorithms.

Key words: generative adversarial networks, transfer learning, vanishing gradients, domain invariant feature, least squares generative adversarial networks loss function