SI-Score: An image dataset for fine-grained analysis of robustness to object location, rotation and size

• URL: http://arxiv.org/abs/2104.04191v1
• Date: Fri, 9 Apr 2021 05:00:49 GMT
• Title: SI-Score: An image dataset for fine-grained analysis of robustness to object location, rotation and size
• Authors: Jessica Yung, Rob Romijnders, Alexander Kolesnikov, Lucas Beyer, Josip Djolonga, Neil Houlsby, Sylvain Gelly, Mario Lucic, Xiaohua Zhai
• Abstract summary: Changing the object location, rotation and size may affect the predictions in non-trivial ways. We perform a fine-grained analysis of robustness with respect to these factors of variation using SI-Score, a synthetic dataset.
• Score: 95.00667357120442
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Before deploying machine learning models it is critical to assess their robustness. In the context of deep neural networks for image understanding, changing the object location, rotation and size may affect the predictions in non-trivial ways. In this work we perform a fine-grained analysis of robustness with respect to these factors of variation using SI-Score, a synthetic dataset. In particular, we investigate ResNets, Vision Transformers and CLIP, and identify interesting qualitative differences between these.

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