Domain Adaptation in Computer Vision. Vision’s Shifting Landscape

What is Domain Adaptation?

Domain adaptation is a machine learning technique that addresses the problem of training a
computer vision model on one dataset (source domain) and applying it to another dataset
(target domain) with different characteristics. This is crucial in real-world scenarios where
environments change, data is limited, or the target domain differs from the training data.

Why is Domain Adaptation Important?

• Real-world diversity: Environments vary, and models must adapt to new conditions.
• Data limitations: Collecting data for every possible scenario is impractical.
• Domain shift: The target domain may change over time.

Common Domain Adaptation Techniques

• Data Augmentation: Generating new training data to mimic target domain conditions.
• Feature Alignment: Finding common features across domains.
• Instance Weighting: Prioritizing examples closer to the target domain.
• Domain Adversarial Training: Training a model to distinguish between domains while also learning a shared representation.

Real-World Applications

• Autonomous Driving: Adapting to different road conditions and weather.
• Medical Imaging: Adapting to different imaging modalities or patient populations.
• Remote Sensing: Adapting to different regions and time periods.

Additional Considerations

• Unsupervised Domain Adaptation: Adapting without labeled target domain data.
• Self-Supervised Domain Adaptation: Using unlabeled target domain data for learning.
• Transfer Learning: Leveraging knowledge from a pre-trained model.

Visuals:

• A diagram illustrating the concept of domain adaptation.
• Examples of source and target domain images.
• A flowchart showing the steps involved in domain adaptation.

Examples:

Domain Adaptation Diagram

Source Domain (Left Side):

Target Domain (Right Side):

Feature Extraction:

Domain Adaptation:

Adapted Features:

Classifier/Model: