Summary of Prediction Accuracy & Reliability: Classification and Object Localization Under Distribution Shift, by Fabian Diet et al.
Prediction Accuracy & Reliability: Classification and Object Localization under Distribution Shift
by Fabian Diet, Moussa Kassem Sbeyti, Michelle Karg
First submitted to arxiv on: 5 Sep 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); 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 The comprehensive analysis explores the impact of natural distribution shifts and weather augmentations on CNNs’ perception performance for real-world traffic data. The study investigates the effects on detection quality, confidence estimation, classification, and object localization tasks under different types of distribution shifts, including adverse weather scenarios and out-of-distribution data. Two common uncertainty quantification methods, Ensembles and Monte-Carlo (MC) Dropout, are benchmarked to evaluate their performance under natural and close-to-natural distribution shift. The analysis also compares the robustness of ConvNeXt-Tiny and EfficientNet-B0 models and identifies the most effective layer combinations for enhancing task performance and confidence estimation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study looks at how well computer vision models, like CNNs, work when they’re exposed to different types of data that are very similar or very different from what they were trained on. The researchers want to know how these models will perform in real-world traffic scenarios with various weather conditions and unusual situations. They also compare two methods for calculating uncertainty in the model’s predictions and find out which one works best under different circumstances. |
Keywords
» Artificial intelligence » Classification » Dropout
