Summary of Optimal Transport For Domain Adaptation Through Gaussian Mixture Models, by Eduardo Fernandes Montesuma et al.
Optimal Transport for Domain Adaptation through Gaussian Mixture Models
by Eduardo Fernandes Montesuma, Fred Maurice Ngolè Mboula, Antoine Souloumiac
First submitted to arxiv on: 18 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (stat.ML)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Machine learning systems operate under the assumption that training and test data are sampled from a fixed probability distribution. The paper discusses the challenges of adapting models to new conditions where this assumption is often violated. To address this, it proposes an optimal transport method between Gaussian Mixture Models (GMMs) for efficient domain adaptation. This approach outperforms previous methods in nine benchmarks, with 85 tasks, and scales well with sample size and dimensionality. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Machine learning models are usually trained on one set of data and tested on another. But what if the conditions that produced these datasets change? The paper solves this problem by creating a new way to adapt models to these changing conditions using a technique called optimal transport. It’s like mapping two different languages so that words can be translated accurately. This method is faster and works better than other methods, even when dealing with large amounts of data. |
Keywords
* Artificial intelligence * Domain adaptation * Machine learning * Probability
