What do you mean by Transfer Learning? How do you apply in Neural Network?
Ans: Transfer learning is a machine learning method where a model developed for a task is reused as the starting point for a model on the other task.
transfer learning methods can be categorized based on the type of traditional ML algorithms involved, such as:
Inductive Transfer learning: In this scenario, the source and target domains are the same, yet the source and target tasks are different from each other. The algorithms try to utilize the inductive biases of the source domain to help improve the target task. Depending upon whether the source domain contains labeled data or not, this can be further divided into two subcategories, similar to multitask learning and self-taught learning, respectively.
Unsupervised Transfer Learning: This setting is similar to inductive transfer itself, with a focus on unsupervised tasks in the target domain. The source and target domains are similar, but the tasks are different. In this scenario, labeled data is unavailable in either of the domains.
Transduce Transfer Learning: In this scenario, there are similarities between the source and target tasks, but the corresponding domains are different. In this setting, the source domain has a lot of labeled data, while the target domain has none. This can be further classified into subcategories, referring to settings where either the feature spaces are different or the marginal probabilities.