Between-Domain Instance Transition Via the Process of Gibbs Sampling in RBM
This addresses domain adaptation for machine learning practitioners by providing a method that does not require target data during training, though it appears incremental as it builds on existing RBM and Gibbs Sampling techniques.
The paper tackles the problem of transferring instances between domains in Transfer Learning by using Gibbs Sampling in Restricted Boltzmann Machines, resulting in enhanced target classification by a considerable ratio on common datasets.
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.