Summary of Online Multi-source Domain Adaptation Through Gaussian Mixtures and Dataset Dictionary Learning, by Eduardo Fernandes Montesuma et al.
Online Multi-Source Domain Adaptation through Gaussian Mixtures and Dataset Dictionary Learning
by Eduardo Fernandes Montesuma, Stevan Le Stanc, Fred Ngolè Mboula
First submitted to arxiv on: 29 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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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 adapting multiple, diverse source domains in real-time to a constantly changing target domain in transfer learning scenarios. The authors introduce a novel approach for fitting Gaussian Mixture Models (GMMs) online using Wasserstein geometry, building upon recent developments in dataset dictionary learning. They propose a strategy for online multi-source domain adaptation (MSDA), which can adapt on the fly to new data streams. Experiments on the Tennessee Eastman Process benchmark demonstrate the effectiveness of this approach. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about adapting multiple sources of information to fit a changing target, like when you’re trying to learn something new from many different places. The authors developed a way to do this online using a type of math called Wasserstein geometry. They tested their method on some challenging data and showed it can adapt quickly to new information. |
Keywords
* Artificial intelligence * Domain adaptation * Transfer learning
