Training large computer vision models from scratch requires significant data and computational resources. Transfer learning offers a practical alternative by reusing knowledge from pre-trained models. While foundational transfer learning techniques like basic fine-tuning are effective, many real-application scenarios demand more sophisticated approaches when adapting models to new tasks or datasets that differ significantly from the original training data.

This chapter examines advanced strategies for model adaptation. We will look at refined methods for selecting between fine-tuning and feature extraction, including layer freezing patterns. You will learn techniques for domain adaptation, addressing situations where the target data distribution ($P_{target}(X, Y)$) differs from the source distribution ($P_{source}(X, Y)$), and the related concept of domain generalization for improving performance on entirely unseen domains. Additionally, we cover few-shot learning methods for building effective models with very limited labeled examples and introduce self-supervised pre-training approaches that learn useful visual representations without relying on manual labels. The objective is to provide you with methods to effectively apply and adjust pre-trained models for specialized tasks and varying data environments.