Cost-effective framework for gradual domain adaptation with multifidelity.

Area of Science:

• Machine Learning
• Artificial Intelligence
• Computer Vision

Background:

• Domain adaptation methods struggle with large domain distances, leading to performance degradation.
• Gradual domain adaptation relies on intermediate domains but fails with insufficient samples or restricted access.
• Existing approaches assume ample data in intermediate domains for self-training, which is often impractical.

Purpose of the Study:

• To develop a novel framework for domain adaptation that overcomes limitations of restricted intermediate domains.
• To address the trade-off between the cost of acquiring data from intermediate domains and prediction accuracy.
• To improve the robustness of domain adaptation when intermediate domain data is scarce or costly.

Main Methods:

• Proposes a framework combining multifidelity and active domain adaptation techniques.
• Integrates strategies to manage varying costs of samples across intermediate domains.
• Employs active learning principles to select informative samples for adaptation.

Main Results:

• Demonstrates improved prediction performance in scenarios with large domain distances and limited intermediate domains.
• Effectively balances the cost of data acquisition with the need for accurate domain adaptation.
• Validation through experiments on real-world datasets confirms the framework's efficacy.

Conclusions:

• The proposed multifidelity and active domain adaptation framework offers a practical solution for challenging domain adaptation problems.
• This approach enhances prediction accuracy while managing data acquisition costs in scenarios with scarce intermediate domain data.
• The method shows significant promise for real-world applications requiring robust domain adaptation.