Feature-Space Planes Searcher: A Universal Domain Adaptation Framework for Interpretability and Computational Efficiency

Domain shift, a problem where AI models perform worse when applied to different data than they were trained on, is a major challenge for deploying deep learning systems. Current methods require expensive retraining of feature extractors (the parts of models that learn to recognize patterns), which is computationally costly and hard to understand. The authors propose Feature-space Planes Searcher (FPS), a new approach that keeps the feature extractor frozen and instead adjusts the decision boundaries (the dividing lines the model uses to classify data) by analyzing geometric patterns in the model's learned feature space, reducing computation while maintaining interpretability.