Hands-On Transfer Learning with Python

Transfer learning strategies

Let's start by first looking at a formal definition for transfer learning and then utilize it to understand different strategies. In their paper, A Survey on Transfer Learning (https://www.cse.ust.hk/~qyang/Docs/2009/tkde_transfer_learning.pdf), Pan and Yang use domain, task, and marginal probabilities to present a framework for understanding transfer learning. The framework is defined as follows:

A domain, D, is defined as a two-element tuple consisting of feature space, , and marginal probability, P(Χ), where Χ is a sample data point.

Here, Χ = {x₁, x₂....x_n} with x_i as a specific vector and Χ . Thus:

A task, ...