SI-Score：用于精细分析对物体位置、旋转和尺寸鲁棒性的图像数据集
计算机视觉与模式识别
2021-04-12 v1 人工智能
机器学习
摘要
在部署机器学习模型之前，评估其鲁棒性至关重要。在用于图像理解的深度神经网络背景下，改变物体位置、旋转和尺寸可能以非平凡的方式影响预测。本工作中，我们使用合成数据集 SI-Score 对这些变化因素的鲁棒性进行了精细分析。特别地，我们研究了 ResNets、Vision Transformers 和 CLIP，并发现了它们之间的有趣定性差异。
引用
@article{arxiv.2104.04191,
title = {SI-Score: An image dataset for fine-grained analysis of robustness to object location, rotation and size},
author = {Jessica Yung and Rob Romijnders and Alexander Kolesnikov and Lucas Beyer and Josip Djolonga and Neil Houlsby and Sylvain Gelly and Mario Lucic and Xiaohua Zhai},
journal= {arXiv preprint arXiv:2104.04191},
year = {2021}
}
备注
4 pages (10 pages including references and appendix), 10 figures. Accepted at the ICLR 2021 RobustML Workshop. arXiv admin note: text overlap with arXiv:2007.08558