Learning explicitly transferable representations for domain adaptation.

Area of Science:

• Computer Science
• Artificial Intelligence
• Machine Learning

Background:

• Domain adaptation aims to improve deep model generalization across datasets with different data distributions.
• Current methods often learn domain-invariant features, which can negatively impact original feature adaptability and increase target domain error.
• This can hinder the performance of deep learning models in real-world applications where data distributions vary.

Purpose of the Study:

• To propose a novel domain adaptation method that adapts classifiers rather than features.
• To address the limitations of existing feature-centric domain adaptation techniques.
• To enhance the generalization ability of deep models in unsupervised domain adaptation scenarios.

Main Methods:

• Developed a classifier adaptation strategy by learning transferable representations from original features.
• Ensured original features remain unchanged while bridging domain distribution gaps.
• Introduced conditional entropy to minimize semantic confusion during representation mapping.

Main Results:

• Achieved new state-of-the-art results on unsupervised domain adaptation.
• Demonstrated the effectiveness of the proposed method on standard and large-scale datasets.
• Validated the approach's ability to improve deep model performance without altering original features.

Conclusions:

• Classifier adaptation is a promising alternative to feature adaptation in domain adaptation.
• The proposed method effectively bridges domain gaps using transferable representations and conditional entropy.
• This approach offers a robust solution for enhancing deep model generalization in varied data distributions.