Summary of Extracting Usable Predictions From Quantized Networks Through Uncertainty Quantification For Ood Detection, by Rishi Singhal and Srinath Srinivasan
Extracting Usable Predictions from Quantized Networks through Uncertainty Quantification for OOD Detection
by Rishi Singhal, Srinath Srinivasan
First submitted to arxiv on: 2 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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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 addresses the growing importance of out-of-distribution (OOD) detection in machine learning, particularly with advancements in network design and increasing task complexity. To improve OOD detection, the authors introduce an Uncertainty Quantification (UQ) technique that estimates the confidence of a pre-trained vision model’s predictions. By leveraging this uncertainty information, the approach can extract valuable predictions while ignoring those with low confidence. Experimental results show that the UQ technique can save up to 80% of misclassified samples, highlighting its potential impact on improving overall performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces an Uncertainty Quantification (UQ) technique to detect out-of-distribution (OOD) data in machine learning models. The authors use a pre-trained vision model and apply the UQ technique to estimate the confidence of predictions. This information is then used to ignore non-confident predictions, improving overall performance. |
Keywords
* Artificial intelligence * Machine learning
