ImageNet-Cartoon and ImageNet-Drawing: two domain shift datasets for ImageNet

Tiago Salvador, Adam M Oberman

Keywords: domain shift, dataset, imagenet, cartoon, drawing

TL;DR: Using GANs and simple image processing to generate two domain shift datasets for ImageNet

Abstract: 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.

Submission Type: Full submission (technical report + code/data)

Co Submission: No, I am not submitting to the dataset and benchmark track and will aim to complete my submission by June 3.