Why Domain Shift Is a Problem

Traditional AI models often deliver precise results only when environmental conditions remain constant. Any changes—such as new camera software and hardware, different lighting conditions, or shifts in production processes—can cause accuracy to drop and lead to new relearning of classification model.

This phenomenon is known as Domain Shift and is one of the key reasons why AI systems frequently produce unreliable results in real-world applications.

Our Solution – Research and Advance Technologies in Action

Our solutions leverage state-of-the-art methods from research and practice:

• Gradient Reversal Layer (GRL): Extracts domain-invariant features for maximum robustness
• FixBi Approach: Combines bi-directional matching with stable pseudo-labels
• Feature Normalization: Guarantees consistent results across varying data sources

mAInthink UDA Framework: research-based and validated for business- and health-critical decision-making

Medical Imaging – A Real-World Example

A doctor can get help with UDA technology from mAInthink not only by fast processing of new images with very good quality, but from automated processing of all previous patient’s images for years.

The quality of correct classification rises in tested images up to 5% and more. Taking into account that the number of radiology images every year in Germany exceeded 150 million (more than 1.3 billion in Europe) and the trend is growing, UDA technology from mAInthink could save a lot of time to health services and help to raise the quality of the service.

Further Application Fields

Our framework delivers maximum value where safety, precision, and stability are essential:

• Medical Imaging: Accurate diagnoses despite differing scanners or image qualities
• Industrial Quality Control: Error-free defect detection even with changing production conditions
• Security & Surveillance: Reliable recognition across day/night cycles and different camera systems
• Financial Analysis: Stable performance despite shifting market conditions and volatile data streams

Conclusion: With mAInthink’s UDA Framework, we not only address the challenge of Domain Shift but also enable industries and healthcare providers to work with AI that is robust, reliable, and future-ready.