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

Area of Science:

• Artificial Intelligence
• Machine Learning
• Computer Vision

Background:

• Domain shift presents a significant challenge in deploying deep learning models, leading to performance degradation when transferring knowledge from labeled source domains to unlabeled target domains.
• Current unsupervised domain adaptation (UDA) methods often involve computationally expensive fine-tuning of feature extractors, limiting their interpretability and scalability.

Purpose of the Study:

• To address the limitations of existing UDA methods by proposing a novel framework that optimizes decision boundaries without altering pre-trained feature extractors.
• To develop an efficient and scalable UDA approach that reduces computational costs and enhances interpretability.

Main Methods:

• The Feature-space Planes Searcher (FPS) framework keeps feature encoders frozen and optimizes decision boundaries by leveraging domain-invariant geometric patterns in the feature space.
• Offline feature extraction allows for full-dataset optimization in a single training cycle, reducing memory and computational requirements.
• An Intra-Class Distance Metric (ICDM) is introduced for unsupervised hyperparameter selection, eliminating the need for target-domain labels.

Main Results:

• FPS achieves competitive performance across standard benchmarks, demonstrating notable gains in various settings and tasks.
• The method demonstrates efficient scalability with large multimodal models.
• FPS shows versatility across diverse domains, including protein structure prediction, remote sensing classification, and earthquake detection.

Conclusions:

• FPS offers a simple, effective, and generalizable framework for unsupervised domain adaptation.
• By focusing on decision boundary alignment rather than feature adaptation, FPS provides a computationally efficient and interpretable alternative to traditional UDA methods.
• The proposed approach maintains the integrity of pre-trained representations while effectively addressing domain shift challenges.