ImageNet-D: A new challenging robustness dataset inspired by domain adaptation
Evgenia Rusak ⋅ Steffen Schneider ⋅ Peter V Gehler ⋅ Oliver Bringmann ⋅ Wieland Brendel ⋅ Matthias Bethge
2022 Oral
in
Workshop: Shift happens: Crowdsourcing metrics and test datasets beyond ImageNet
in
Workshop: Shift happens: Crowdsourcing metrics and test datasets beyond ImageNet
Abstract
We propose a new challenging dataset to benchmark robustness of ImageNet-trained models: ImageNet-D. ImageNet-D has six different domains (Real'',Painting'', Clipart'',Sketch'', Infograph'' andQuickdraw''). We show that even state-of-the-art models struggle on this dataset and find that they make well-interpretable errors.
Video
Chat is not available.
Successful Page Load