Summary of Consistency-guided Temperature Scaling Using Style and Content Information For Out-of-domain Calibration, by Wonjeong Choi et al.
Consistency-Guided Temperature Scaling Using Style and Content Information for Out-of-Domain Calibration
by Wonjeong Choi, Jungwuk Park, Dong-Jun Han, Younghyun Park, Jaekyun Moon
First submitted to arxiv on: 22 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); 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 In this paper, researchers tackle the long-standing issue of deep neural networks’ robustness against domain shifts. While most works focus on improving model accuracy, this study prioritizes calibration performance, a crucial aspect for trustworthy AI systems. The authors propose consistency-guided temperature scaling (CTS), a novel approach that enhances out-of-domain (OOD) calibration by leveraging mutual supervision among data samples in source domains. This strategy addresses over-confidence stemming from inconsistent sample predictions, a major obstacle to OOD calibration. Experimental results show that CTS outperforms existing works, achieving superior OOD calibration performance on various datasets. This breakthrough can be applied directly to trustworthy AI systems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps make artificial intelligence more reliable and accurate. The researchers found that most AI models aren’t good at adapting to new situations or data they haven’t seen before. They want to improve this by making sure the model is confident in its predictions, even when it’s not seeing things exactly like it did during training. To do this, they created a new way of adjusting the model’s “temperature” setting, which helps the model make more accurate and reliable decisions. The new method works well on different types of data and can be used to make AI systems more trustworthy. |
Keywords
* Artificial intelligence * Stemming * Temperature
