Summary of Investigating Calibration and Corruption Robustness Of Post-hoc Pruned Perception Cnns: An Image Classification Benchmark Study, by Pallavi Mitra et al.
Investigating Calibration and Corruption Robustness of Post-hoc Pruned Perception CNNs: An Image Classification Benchmark Study
by Pallavi Mitra, Gesina Schwalbe, Nadja Klein
First submitted to arxiv on: 31 May 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 In this research paper, the authors investigate the trade-off between uncertainty calibration, natural corruption robustness, and performance for post-hoc CNN pruning techniques in image classification tasks. They demonstrate that post-hoc pruning improves model uncertainty calibration, performance, and natural corruption robustness, paving the way for safe and robust embedded deployment of Convolutional Neural Networks (CNNs). By exploring the interplay between these factors, the authors provide insights into how to achieve state-of-the-art performance while ensuring safety-critical applications meet proper uncertainty calibration and natural corruption robustness requirements. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper looks at how to make computer vision models work safely on devices with limited resources. The authors show that a technique called post-hoc pruning can help improve the model’s ability to make accurate predictions without getting too confident, while also being able to handle small mistakes in the input data. This could be important for using these models in real-world applications where safety is crucial. |
Keywords
* Artificial intelligence * Cnn * Image classification * Pruning
