Summary of Uncertainty Calibration with Energy Based Instance-wise Scaling in the Wild Dataset, by Mijoo Kim and Junseok Kwon
Uncertainty Calibration with Energy Based Instance-wise Scaling in the Wild Dataset
by Mijoo Kim, Junseok Kwon
First submitted to arxiv on: 17 Jul 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 investigates the use of deep neural networks (DNNs) for multi-class classification tasks and proposes a novel instance-wise calibration method to ensure reliable AI systems. The authors highlight that many DNNs lack the ability to represent uncertainty, often exhibiting excessive confidence even when making incorrect predictions. To address this issue, they introduce an energy model-based method that incorporates energy scores instead of softmax confidence scores, allowing for adaptive consideration of DNN uncertainty for each prediction within a logit space. The proposed method is shown to consistently maintain robust performance across various scenarios, including in-distribution and out-of-distribution inputs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps make artificial intelligence (AI) systems more reliable by finding ways to show how sure or unsure they are about their predictions. Right now, many AI systems pretend to be super confident even when they’re wrong. This can be a problem, especially in situations where the AI’s mistake could have serious consequences. To fix this, the researchers developed a new way to “calibrate” AI models so they give more accurate uncertainty estimates. They tested their method and found it works well for both regular and unusual inputs. |
Keywords
» Artificial intelligence » Classification » Softmax
