The difference in data distributions among related, but different domains is a long standing
problem for knowledge adaptation. A new method to transform the source domain …

With the development of deep transfer learning, the generalization abilities of models in
similar scenarios have been significantly improved. However, for regression tasks, either the …

Abstract Knowledge transfer from multiple source domains to a target domain is crucial in
transfer learning. Most existing methods are focused on learning weights for different …

Recent years have witnessed an increased interest in transfer learning. Despite the vast
amount of research performed in this field, there are remaining challenges in applying the …

Traditional machine learning methods require the assumption that training and test data are
drawn from the same distribution, which proves challenging in real-world applications …

Transfer learning allows practitioners to recognize and apply knowledge learned in previous
tasks (source task) to new tasks or new domains (target task), which share some …

Previous transfer learning methods based on deep network assume the knowledge should
be transferred between the same hidden layers of the source domain and the target …

Transfer learning (TL) is a machine learning paradigm designed for the problem where the
training and test data are from different domains. Existing TL approaches mostly assume that …

Transfer learning methods have been successfully applied in solving a wide range of real-
world problems. However, there is almost no attempt of effectively using these methods in …

Transfer learning can enhance classification performance of a target domain with insufficient
training data by utilizing knowledge relating to the target domain from source domain …