The main hypothesis in Ilyas et al. (2019) happens to be a special case of a more general principle that is commonly accepted in the robustness to distributional shift literature

An example project using webpack and svelte-loader and ejs to inline SVGs

An example project using webpack and svelte-loader and ejs to inline SVGs

An experiment showing adversarial robustness makes neural style transfer work on a non-VGG architecture

Refining the source of adversarial examples

Section 3.2 of Ilyas et al. (2019) shows that training a model on only adversarial errors leads to non-trivial generalization on the original test set. We show that these experiments are a specific case of learning from errors.