Adaptive Transferred-profile Likelihood Learning
Son N. Tran, Artur d’Avila Garcez · 2016
The recent success of representation learning is built upon the learning of relevant features, in particular from unlabelled data available in different domains. This raises the question of how to transfer and reuse such knowledge effectively so that the learning of a new task can be made easier or be improved. This poses a difficult challenge for the area of transfer learning where there is no label in the source data, and no source data is ever transferred to the target domain. In previous work, the most capable approach has been self-taught learning which, however, relies heavily upon the compatibility across the domains. In this paper, we propose a novel transfer learning framework called Adaptive Transferred-profile Likelihood Learning (aTPL), which performs adaptation on the representations to be transferred, so that they become more compatible with the target domain. At the same time, it learns supplementary knowledge about the target domain. Experiments on five images datasets and a sentiment dataset demonstrate the effectiveness of the approach in comparison with self-taught learning and other common feature extraction methods. The results also indicate that the new transfer method is less sensitive to negative transfer.