Multi-representation adaptation network for cross-domain image classification.

Area of Science:

• Computer Vision
• Machine Learning

Background:

• Labeling image data for machine learning is costly and time-consuming.
• Domain adaptation is a viable solution using labeled data from related but distinct domains.
• Current methods often use single representations, potentially missing crucial information.

Purpose of the Study:

• To enhance cross-domain image classification accuracy.
• To address limitations of single-representation alignment in domain adaptation.

Main Methods:

• Propose Multi-Representation Adaptation (MRA) using a hybrid Inception Adaptation Module (IAM).
• Introduce the Multi-Representation Adaptation Network (MRAN) for aligning multiple representations.
• Extend Maximum Mean Discrepancy (MMD) for calculating adaptation loss.

Main Results:

• MRAN effectively captures information from diverse aspects by aligning multiple representations.
• The approach integrates seamlessly with existing feed-forward models via IAM.
• Efficient training is achieved through standard back-propagation.

Conclusions:

• MRAN significantly improves classification accuracy in cross-domain scenarios.
• The proposed method offers a flexible and efficient solution for domain adaptation.
• Experimental results on benchmark datasets validate the effectiveness of MRAN.