Summary of Accurate and Reliable Predictions with Mutual-transport Ensemble, by Han Liu et al.
Accurate and Reliable Predictions with Mutual-Transport Ensemble
by Han Liu, Peng Cui, Bingning Wang, Jun Zhu, Xiaolin Hu
First submitted to arxiv on: 30 May 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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 A recent paper proposes the “mutual-transport ensemble” (MTE) to improve uncertainty calibration in deep neural networks (DNNs). DNNs have achieved high prediction accuracy, but this alone is not sufficient for safety-critical applications where reliable uncertainty estimates are crucial. The MTE approach introduces a co-trained auxiliary model and adaptively regularizes the cross-entropy loss using Kullback-Leibler divergence between the primary and auxiliary models’ prediction distributions. This method can simultaneously enhance both accuracy and uncertainty calibration, as demonstrated on various benchmarks including CIFAR-100. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper suggests a new way to make deep neural networks (DNNs) more reliable. DNNs are very good at making predictions, but they often don’t know how sure they are about their answers. This is important because we need to know if our models are uncertain or confident in their predictions. The authors propose an idea called the “mutual-transport ensemble” (MTE), which helps make DNNs more accurate and better at estimating uncertainty. They tested this approach on several datasets, including images of animals and objects. |
Keywords
* Artificial intelligence * Cross entropy
