关键词: 迁移学习, 经验风险最小化（ERM）, 泛化的经验风险最小化（GERM）, 区别性知识利用, 负迁移

Abstract: Current transfer learning model studies the source data for future target inferences within a major view that the whole source data should be used to explore the shared knowledge structure. However, due to the limited availability of human ranked source domain, this assumption may not hold due to the fact that not all prior knowledge in the source domain is correlative to the target domain in most real-world applications. This paper proposes a general framework referred to discriminative knowledge-leverage (KL) based on generalized empirical risk minimization (GERM) transfer learning, where the empirical risk minimization (ERM) principle is generalized to the transfer learning setting. Additionally, this paper theoretically shows the upper bound of generalized ERM (GERM) for the practical discriminative transfer learning. The proposed method can alleviate negative transfer by automatically discovering useful objects from source domain. Extensive experiments verify that the proposed method can significantly outperform the state-of-the-art transfer learning methods on several artificial/public datasets.

Key words: transfer learning, empirical risk minimization (ERM), generalized empirical risk minimization (GERM), discriminative knowledge-leverage, negative transfer