Summary of Lighter, Better, Faster Multi-source Domain Adaptation with Gaussian Mixture Models and Optimal Transport, by Eduardo Fernandes Montesuma et al.
Lighter, Better, Faster Multi-Source Domain Adaptation with Gaussian Mixture Models and Optimal Transport
by Eduardo Fernandes Montesuma, Fred Ngolè Mboula, Antoine Souloumiac
First submitted to arxiv on: 16 Apr 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper tackles the challenge of Multi-Source Domain Adaptation (MSDA), a crucial task in transfer learning where multiple heterogeneous, labeled source probability measures are adapted towards an unlabeled target measure. The authors propose a novel framework based on Optimal Transport (OT) and Gaussian Mixture Models (GMMs). This framework has two key advantages: efficient OT between GMMs via linear programming and a convenient model for supervised learning, particularly classification, where components in the GMM can be associated with existing classes. The authors also propose two new strategies for MSDA: GMM-Wasserstein Barycenter Transport (WBT) and GMM-Dataset Dictionary Learning (DaDiL). These methods are empirically evaluated on four benchmarks in image classification and fault diagnosis, showing improved performance compared to prior art while being faster and involving fewer parameters. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us adapt multiple sources of labeled information to a new, unknown target. Think of it like training a model on many different types of pictures and then using that knowledge to classify new, unseen images. The authors created a new way to do this using a combination of two powerful tools: Optimal Transport and Gaussian Mixture Models. This new method is faster and more efficient than previous approaches, making it useful for tasks like image classification and fault diagnosis. |
Keywords
» Artificial intelligence » Classification » Domain adaptation » Image classification » Probability » Supervised » Transfer learning
