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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)

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