Timezone: »

 
ImageNet-Cartoon and ImageNet-Drawing: two domain shift datasets for ImageNet
Tiago Salvador · Adam Oberman
Event URL: https://openreview.net/forum?id=YlAUXhjwaQt »

Benchmarking the robustness to distribution shifts traditionally relies on dataset collection which is typically laborious and expensive, in particular for datasets with a large number of classes like ImageNet. An exception to this procedure is ImageNet-C (Hendrycks & Dietterich, 2019), a dataset created by applying common real-world corruptions at different levels of intensity to the (clean) ImageNet images. Inspired by this work, we introduce ImageNet-Cartoon and ImageNet-Drawing, two datasets constructed by converting ImageNet images into cartoons and colored pencil drawings, using a GAN framework (Wang & Yu, 2020) and simple image processing (Lu et al., 2012), respectively.

Author Information

Tiago Salvador (McGill University)
Adam Oberman (McGill University)

Related Events (a corresponding poster, oral, or spotlight)

More from the Same Authors