Poster
in
Workshop: Shift happens: Crowdsourcing metrics and test datasets beyond ImageNet
SI-Score
Jessica Yung · Rob Romijnders · Alexander Kolesnikov · Lucas Beyer · Josip Djolonga · Neil Houlsby · Sylvain Gelly · Mario Lucic · Xiaohua Zhai
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. SI-Score is a synthetic image dataset that allows one to do fine-grained analysis of robustness to object location, rotation and size.
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