  In this paper, we study the problem of transfer learning with the attribute
data. In the transfer learning problem, we want to leverage the data of the
auxiliary and the target domains to build an effective model for the
classification problem in the target domain. Meanwhile, the attributes are
naturally stable cross different domains. This strongly motives us to learn
effective domain transfer attribute representations. To this end, we proposed
to embed the attributes of the data to a common space by using the powerful
convolutional neural network (CNN) model. The convolutional representations of
the data points are mapped to the corresponding attributes so that they can be
effective embedding of the attributes. We also represent the data of different
domains by a domain-independent CNN, ant a domain-specific CNN, and combine
their outputs with the attribute embedding to build the classification model.
An joint learning framework is constructed to minimize the classification
errors, the attribute mapping error, the mismatching of the domain-independent
representations cross different domains, and to encourage the the neighborhood
smoothness of representations in the target domain. The minimization problem is
solved by an iterative algorithm based on gradient descent. Experiments over
benchmark data sets of person re-identification, bankruptcy prediction, and
spam email detection, show the effectiveness of the proposed method.
