Before revision:  We also show how viewmaker views can be combined with handcrafted views to improve robustness to common image corruptions . Our method demonstrates that learned views are a promising way to reduce the amount of expertise and effort needed for unsupervised learning, potentially extending its benefits to a much wider set of domains. 
After revision:  We also show how viewmaker views can be combined with handcrafted views to improve robustness to common image corruptions and can increase transfer performance in cases where handcrafted views are less explored. These results suggest that viewmakers may provide a path towards more general representation learning algorithms -- reducing the domain expertise and effort needed for unsupervised learning, potentially extending its benefits to a much wider set of domains.