Are Synthetic Corruptions A Reliable Proxy For Real-World Corruptions?
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arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866916725943959552 |
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| author | Agnihotri, Shashank Schader, David Sharei, Nico Kaçar, Mehmet Ege Keuper, Margret |
| author_facet | Agnihotri, Shashank Schader, David Sharei, Nico Kaçar, Mehmet Ege Keuper, Margret |
| contents | Deep learning (DL) models are widely used in real-world applications but remain vulnerable to distribution shifts, especially due to weather and lighting changes. Collecting diverse real-world data for testing the robustness of DL models is resource-intensive, making synthetic corruptions an attractive alternative for robustness testing. However, are synthetic corruptions a reliable proxy for real-world corruptions? To answer this, we conduct the largest benchmarking study on semantic segmentation models, comparing performance on real-world corruptions and synthetic corruptions datasets. Our results reveal a strong correlation in mean performance, supporting the use of synthetic corruptions for robustness evaluation. We further analyze corruption-specific correlations, providing key insights to understand when synthetic corruptions succeed in representing real-world corruptions. Open-source Code: https://github.com/shashankskagnihotri/benchmarking_robustness/tree/segmentation_david/semantic_segmentation |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_04835 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Are Synthetic Corruptions A Reliable Proxy For Real-World Corruptions? Agnihotri, Shashank Schader, David Sharei, Nico Kaçar, Mehmet Ege Keuper, Margret Computer Vision and Pattern Recognition Deep learning (DL) models are widely used in real-world applications but remain vulnerable to distribution shifts, especially due to weather and lighting changes. Collecting diverse real-world data for testing the robustness of DL models is resource-intensive, making synthetic corruptions an attractive alternative for robustness testing. However, are synthetic corruptions a reliable proxy for real-world corruptions? To answer this, we conduct the largest benchmarking study on semantic segmentation models, comparing performance on real-world corruptions and synthetic corruptions datasets. Our results reveal a strong correlation in mean performance, supporting the use of synthetic corruptions for robustness evaluation. We further analyze corruption-specific correlations, providing key insights to understand when synthetic corruptions succeed in representing real-world corruptions. Open-source Code: https://github.com/shashankskagnihotri/benchmarking_robustness/tree/segmentation_david/semantic_segmentation |
| title | Are Synthetic Corruptions A Reliable Proxy For Real-World Corruptions? |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2505.04835 |