Summary of Uncertainty-guided Alignment For Unsupervised Domain Adaptation in Regression, by Ismail Nejjar et al.
Uncertainty-Guided Alignment for Unsupervised Domain Adaptation in Regression
by Ismail Nejjar, Gaetan Frusque, Florent Forest, Olga Fink
First submitted to arxiv on: 24 Jan 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- 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 Unsupervised Domain Adaptation for Regression (UDAR) is a machine learning challenge that aims to adapt models from a labeled source domain to an unlabeled target domain for regression tasks. Traditional feature alignment methods, successful in classification, often prove ineffective for regression due to the correlated nature of regression features. The authors propose Uncertainty-Guided Alignment (UGA), a novel method that integrates predictive uncertainty into the feature alignment process using Evidential Deep Learning. UGA predicts both target values and their associated uncertainties, guiding the alignment process and fusing information within the embedding space to mitigate issues such as feature collapse in out-of-distribution scenarios. The authors evaluate UGA on two computer vision benchmarks and a real-world battery state-of-charge prediction across different manufacturers and operating temperatures. Across 52 transfer tasks, UGA on average outperforms existing state-of-the-art methods, not only improving adaptation performance but also providing well-calibrated uncertainty estimates. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you have a model that can predict something very well, like how hot a battery is. But what if the batteries are from different manufacturers or have different temperatures? The model might not work as well because it’s never seen these new situations before. This problem is called unsupervised domain adaptation for regression. Researchers tried to solve this by making the model learn more about the differences between the old and new situations, but they didn’t do very well. A new method was developed that uses a special type of learning called Evidential Deep Learning. It predicts not only how hot the battery is but also how certain it is about its prediction. This helps the model make better predictions when it sees new situations. The new method worked much better than old methods, and it’s useful for lots of things like predicting battery temperature or image classification. |
Keywords
* Artificial intelligence * Alignment * Classification * Deep learning * Domain adaptation * Embedding space * Image classification * Machine learning * Regression * Temperature * Unsupervised
