Summary of Fast and Reliable Uncertainty Quantification with Neural Network Ensembles For Industrial Image Classification, by Arthur Thuy et al.
Fast and reliable uncertainty quantification with neural network ensembles for industrial image classification
by Arthur Thuy, Dries F. Benoit
First submitted to arxiv on: 15 Mar 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 This paper investigates efficient neural network (NN) ensembles for image classification tasks in industrial processes. Traditional NNs struggle with out-of-distribution data, making confident yet incorrect predictions. To address this issue, the proposed models should quantify their uncertainty, indicating when outputs are trustworthy. Deep ensembles have shown strong performance but are computationally expensive. The study compares and proposes a novel Diversity Quality metric to evaluate efficient NN ensembles in image classification tasks. Results highlight the batch ensemble as a cost-effective alternative to deep ensembles, matching accuracy and uncertainty while reducing training time, test time, and memory storage. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making sure neural networks are reliable when classifying images for industrial processes. Sometimes these networks get confused when they see new or unusual objects. To fix this, the networks should be able to say how confident they are in their answers. The study compares different ways to make neural network ensembles work better and proposes a new way to measure how well they do. It finds that one type of ensemble is fast and good at classifying images while also being uncertain when it shouldn’t be. |
Keywords
* Artificial intelligence * Image classification * Neural network
