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Optimal Transport for Domain Adaptation.
IEEE Transactions on Pattern Analysis and Machine Intelligence
|October 11, 2016
Summary
This study introduces a new domain adaptation method using optimal transportation to align data representations, improving model robustness across different observation systems. The approach effectively leverages labeled source data and distributions from both domains for better performance.
Area of Science:
- Machine Learning
- Computer Vision
- Data Analytics
Background:
- Domain adaptation is crucial for robust machine learning models facing diverse data sources.
- Existing methods often struggle to generalize effectively across different observation systems.
- Domain-invariant representations offer a promising strategy for unified model training.
Purpose of the Study:
- To develop a novel domain adaptation technique for aligning data representations.
- To enhance model robustness and performance when applied to new domains.
- To exploit both labeled source data and unlabeled target data distributions.
Main Methods:
- A regularized unsupervised optimal transportation model is proposed for representation alignment.
- A transportation plan is learned to match probability density functions (PDFs) of source and target domains.
- Labeled source samples are constrained to remain close during the transportation process.
Main Results:
- The proposed method consistently outperforms state-of-the-art approaches on visual domain adaptation tasks.
- Experiments demonstrate improved performance on domain-invariant deep learning features.
- The approach shows adaptability to semi-supervised learning scenarios with limited target domain labels.
Conclusions:
- The optimal transportation-based domain adaptation method offers a powerful way to align data representations.
- This technique enhances model generalization and robustness across different domains.
- The method is effective and versatile, applicable to both unsupervised and semi-supervised learning settings.