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In this paper, we confront the problem of deep learning's big labeled datarequirements, offer a rule based strategy for extreme augmentation of smalldata sets and apply that strategy with the image to image translation model byIsola et al. (2016) to automate cel style cartoon coloring with very limitedtraining data. While our experimental results using geometric rules andtransformations demonstrate the performance of our methods on an imagetranslation task with industry applications in art, design and animation, wealso propose the use of rules on partial data sets as a generalizable smalldata strategy, potentially applicable across data types and domains.

DragonPaint: Rule based bootstrapping for small data with an application to cartoon coloring