Summary of C-adapter: Adapting Deep Classifiers For Efficient Conformal Prediction Sets, by Kangdao Liu et al.
C-Adapter: Adapting Deep Classifiers for Efficient Conformal Prediction Sets
by Kangdao Liu, Hao Zeng, Jianguo Huang, Huiping Zhuang, Chi-Man Vong, Hongxin Wei
First submitted to arxiv on: 12 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 Conformal prediction, a technique for uncertainty quantification, is often used post-hoc with trained classifiers to optimize predictive efficiency. Conformal Training regularizes the classifier to minimize average prediction set size at a specific error rate, but this regularization term can degrade classification accuracy and lead to suboptimal efficiency. To address this, we introduce C-Adapter (C-Adapter), an adapter-based tuning method that enhances conformal predictor efficiency without sacrificing accuracy. We implement the adapter as intra order-preserving functions and tune it with our proposed loss that maximizes discriminability between correct and random data-label pairs. Our approach produces high non-conformity scores for incorrect labels, enhancing prediction set efficiency across coverage rates. Extensive experiments demonstrate C-Adapter’s effectiveness in adapting various classifiers for efficient prediction sets and improving conformal training. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making predictions more accurate and less uncertain. It uses a technique called Conformal Prediction to do this. The problem is that the current method, called Conformal Training, can make things worse by sacrificing accuracy for efficiency. To fix this, the researchers created something called C-Adapter, which makes adjustments to improve prediction accuracy without making it less efficient. They tested C-Adapter on different types of data and found that it works well and improves overall performance. |
Keywords
* Artificial intelligence * Classification * Regularization
