Summary of Credal Wrapper Of Model Averaging For Uncertainty Estimation on Out-of-distribution Detection, by Kaizheng Wang et al.
Credal Wrapper of Model Averaging for Uncertainty Estimation on Out-Of-Distribution Detection
by Kaizheng Wang, Fabio Cuzzolin, Keivan Shariatmadar, David Moens, Hans Hallez
First submitted to arxiv on: 23 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 This paper proposes a novel approach called credal wrapper for model averaging in Bayesian neural networks (BNNs) and deep ensembles. The credal wrapper method represents model uncertainties as credal sets, providing upper and lower probability bounds per class. This allows for improved uncertainty estimation in classification tasks. The authors demonstrate the effectiveness of their method through extensive experiments on multiple out-of-distribution detection benchmarks using various dataset pairs and network architectures. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to make computers better at guessing what something is, like recognizing pictures or understanding speech. It’s called “credal wrapper” and it helps computers be more sure when they’re not 100% certain. This is important because sometimes computers are really good at one thing but get confused when things don’t quite fit. The scientists tested their new method on lots of different kinds of computer problems and found that it worked better than other methods. |
Keywords
» Artificial intelligence » Classification » Probability
