“What to transfer” asks which part of knowledge can be transferred across domains or tasks. Some knowledge is specific for individual domains or tasks, and some knowledge may be common between different domains such that they may help improve performance for the target domain or task.
how to transfer
After discovering which knowledge can be transferred, learning algorithms need to be developed to transfer the knowledge, which corresponds to the “how to transfer” issue.
when to transfer
Most current work on transfer learning focuses on “What to transfer” and “How to transfer”, by implicitly assuming that the source and target domains be related to each other. However, how to avoid negative transfer is an important open issue that is attracting more and more attention in the future
The fundamental motivation for Transfer learning in the field of machine learning was discussed in a NIPS-95 workshop on “Learning to Learn” 1, which focused on the need for lifelong machine-learning methods that retain and reuse previously learned knowledge
How to transfer?(3种不同的迁移模式, based on the definition of transfer learning)
inductive transfer learning (推导迁移学习)
transductive transfer learning (转导迁移学习)
unsupervised transfer learning (无监督迁移学习)
Transfer learning is classified to three different settings: inductive transfer learning, transductive transfer learning and unsupervised transfer learning. Most previous works focused on the former two settings. Unsupervised transfer learning may attract more and more attention in the future.
无监督学习：Unsupervised learning allows us to approach problems with little or no idea what our results should look like. We can derive structure from data where we don’t necessarily know the effect of the variables.