Summary of Discriminant Distance-aware Representation on Deterministic Uncertainty Quantification Methods, by Jiaxin Zhang et al.
Discriminant Distance-Aware Representation on Deterministic Uncertainty Quantification Methods
by Jiaxin Zhang, Kamalika Das, Sricharan Kumar
First submitted to arxiv on: 20 Feb 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 This paper introduces a novel approach for deterministic uncertainty estimation in deep learning models called Discriminant Distance-Awareness Representation (DDAR). The method constructs a DNN model that incorporates prototypes in its latent representations, allowing it to analyze valuable feature information. By leveraging a distinction maximization layer over optimal trainable prototypes, DDAR learns a discriminant distance-awareness representation, overcoming the limitations of traditional deterministic uncertainty methods. The approach is shown to be flexible and architecture-agnostic, outperforming state-of-the-art methods on multiple benchmark problems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Uncertainty estimation in deep learning models is important for safety-critical systems. This paper introduces a new way to do this called DDAR (Discriminant Distance-Awareness Representation). It’s like building a special kind of map that helps the model understand what it’s seeing. This makes the model better at knowing when it’s unsure, which is important for making good decisions. The method is easy to use and works well on different problems. |
Keywords
* Artificial intelligence * Deep learning
