Transfer learning aims to leverage the knowledge in the source domain to facilitate the learning tasks in the tar- get domain. It has attracted extensive research interests recently due to its effectiveness in a wide range of applica- Tions. The general idea of the existing methods is to utilize the common latent structure shared across domains as the bridge for knowledge transfer. These methods usually model the common latent structure by using either the marginal distribution or the conditional distribution. However, with- out exploring the duality between these two distributions, these single bridge methods may not achieve optimal capa- bility of knowledge transfer. In this paper, we propose a novel approach, Dual Transfer Learning (DTL), which simultaneously learns the marginal and conditional distributions, and exploits the du- Ality between them in a principled way. The key idea behind DTL is that learning one distribution can help to learn the other. This duality property leads to mutual reinforcement when adapting both distributions across domains to trans- fer knowledge. The proposed method is formulated as an optimization problem based on joint nonnegative matrix tri- factorizations (NMTF). The two distributions are learned from the decomposed latent factors that exhibit the duality property. An efficient alternating minimization algorithm is developed to solve the optimization problem with con- vergence guarantee. Extensive experimental results demon- strate that DTL is more effective than alternative transfer learning methods.