Transfer learning: a friendly introduction.

Area of Science:

• Computer Science
• Artificial Intelligence

Background:

• Machine Learning (ML) models traditionally require extensive data for training and testing.
• Conventional ML struggles with limited data distributions, impacting prediction accuracy.
• Transfer Learning (TL) has emerged as a key ML subfield to address these challenges.

Purpose of the Study:

• To explore the domain and scope of Transfer Learning (TL).
• To cite situational uses of TL based on their application periods.
• To provide an in-depth focus on various TL techniques and their contributions.

Main Methods:

• Review of existing literature on Transfer Learning.
• Categorization of TL techniques including Inductive TL, Transductive TL, and Unsupervised TL.
• Analysis of sample selection and domain adaptation strategies within TL.

Main Results:

• Transfer Learning (TL) offers efficient solutions for ML tasks with limited data.
• TL enables faster model development by utilizing pre-existing knowledge.
• Identified various applications and situational uses of TL across different periods.

Conclusions:

• Transfer Learning (TL) is a powerful ML technique for improving efficiency and performance.
• Further research into TL techniques like Inductive, Transductive, and Unsupervised TL is warranted.
• The paper provides a foundation for understanding TL's contributions and future directions.