Summary of Uncertainty Estimation by Density Aware Evidential Deep Learning, By Taeseong Yoon and Heeyoung Kim
Uncertainty Estimation by Density Aware Evidential Deep Learning
by Taeseong Yoon, Heeyoung Kim
First submitted to arxiv on: 13 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 Evidential deep learning (EDL) has achieved remarkable success in uncertainty estimation, but there is still room for improvement, particularly in out-of-distribution (OOD) detection and classification tasks. The limitations of EDL stem from its inability to quantify the distance between testing examples and training data when estimating uncertainty, as well as its parameterization of concentration parameters affecting classification performance. To address these issues, we introduce Density Aware Evidential Deep Learning (DAEDL), a novel method that integrates feature space density with EDL output during prediction. DAEDL also resolves conventional parameterization limitations through a novel approach. Theoretical analysis demonstrates favorable properties of DAEDL, which achieves state-of-the-art performance across various tasks related to uncertainty estimation and classification. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making computers better at guessing what they don’t know. Right now, these machines are pretty good at guessing things within a certain range, but struggle when trying to figure out things that are very different from what they’ve learned before. The problem is that these computers have trouble understanding how much they don’t know. To fix this, the authors created a new way of making predictions called Density Aware Evidential Deep Learning (DAEDL). This new approach takes into account how unusual or normal something is compared to what it’s learned before. The results show that DAEDL is much better at guessing things than the old way, and can be used for many different tasks. |
Keywords
» Artificial intelligence » Classification » Deep learning
