Advanced · Research

Domain Adaptation

Visual diagram · (in preparation) · Math · (in preparation) · Worked example · 3 difficulty levels.

TL;DR. Techniques for applying a model trained on a source domain to a different but related target domain with limited labeled data.

Technical Definition

Techniques for applying a model trained on a source domain to a different but related target domain with limited labeled data.

How it works

Domain adaptation addresses the challenge of dataset shift, where a model trained on one data distribution (source domain) performs poorly on a different but related distribution (target domain). The goal is to transfer knowledge from the source to the target by minimizing the discrepancy between their distributions. This is particularly useful in scenarios where labeling data in the target domain is expensive or impossible, enhancing model generalization across different environments.

Related Concepts

• Generalization — A model's ability to perform well on new, unseen data — not just its training set.