Deep learning models are representative of what is also known as inductive learning. The objective for inductive-learning algorithms is to infer a mapping from a set of training examples. For instance, in cases of classification, the model learns mapping between input features and class labels. In order for such a learner to generalize well on unseen data, its algorithm works with a set of assumptions related to the distribution of the training data. These sets of assumptions are known as inductive bias.
The inductive bias or assumptions can be characterized by multiple factors, such as the hypothesis space it restricts to and the search process through the hypothesis space. Thus, these biases impact how and what is learned by the model on the given task and domain.
Inductive transfer techniques utilize the inductive biases of the source task...
