Summary of Parce: Probabilistic and Reconstruction-based Competency Estimation For Cnn-based Image Classification, by Sara Pohland and Claire Tomlin
PaRCE: Probabilistic and Reconstruction-based Competency Estimation for CNN-based Image Classification
by Sara Pohland, Claire Tomlin
First submitted to arxiv on: 22 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG); 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 presents a novel approach called PaRCE (Probabilistic and Reconstruction-Based Competency Estimation) to quantify uncertainty in convolutional neural networks (CNNs) for image classification tasks. Unlike previous works that focused on specific aspects, such as out-of-distribution detection or anomaly localization, the authors aim to develop a holistic method that estimates CNN confidence across various sources of uncertainty. The proposed PaRCE method compares favorably with existing approaches for uncertainty quantification and out-of-distribution detection. It can effectively distinguish between correctly classified, misclassified, and out-of-distribution samples with anomalous regions, as well as image modifications resulting in high, medium, and low prediction accuracy. The authors also demonstrate the approach’s ability to localize anomalies within an image and generate interpretable scores that reliably capture a holistic notion of perception model confidence. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research develops a new way to understand how sure convolutional neural networks (CNNs) are about what they see. CNNs are really good at recognizing images, but sometimes they can be too confident or get confused. The researchers created a method called PaRCE that can measure the uncertainty of CNNs and detect when something is unusual in an image. This helps to improve the accuracy of image classification tasks. The new approach outperforms other methods for detecting unusual images and can even pinpoint where the problem lies within an image. The results show that the method provides a reliable way to understand how confident or uncertain a CNN is about what it sees. |
Keywords
» Artificial intelligence » Cnn » Image classification
