  In this paper, we present a new idea for Transfer Learning (TL) based on
Gibbs Sampling. Gibbs sampling is an algorithm in which instances are likely to
transfer to a new state with a higher possibility with respect to a probability
distribution. We find that such an algorithm can be employed to transfer
instances between domains. Restricted Boltzmann Machine (RBM) is an energy
based model that is very feasible for being trained to represent a data
distribution and also for performing Gibbs sampling. We used RBM to capture
data distribution of the source domain and use it in order to cast target
instances into new data with a distribution similar to the distribution of
source data. Using datasets that are commonly used for evaluation of TL
methods, we show that our method can successfully enhance target classification
by a considerable ratio. Additionally, the proposed method has the advantage
over common DA methods that it needs no target data during the process of
training of models.
