Unpaired ImagetoSketch Translation Network for Sketch Synthesis Imagetosketch translation is to learn the mapping between an image and a corresponding human drawn sketch. Machine can be trained to mimic the human drawing process using a training set of aligned imagesketch pairs. However, to collect such paired data is quite expensive or even unavailable for many cases since sketches exhibit various level of abstractness and drawing preferences. Hence we present an approach for learning an imagetosketch translation network via unpaired examples. A translation network, which can translate the representation in image latent space to sketch domain, is trained in unsupervised setting. To prevent the problem of representation shifting in crossdomain translation, a novel cycle consistency loss is explored. Experimental results on sketch recognition and sketchbased image retrieval demonstrate the effectiveness of our approach.

GANILLA: Generative adversarial networks for image to illustration translation Abstract   In this paper, we explore illustrations in childrenu0027s books as a new domain in unpaired imagetoimage translation. We show that although the current stateoftheart imagetoimage translation models successfully transfer either the style or the content, they fail to transfer both at the same time. We propose a new generator network to address this issue and show that the resulting network strikes a better balance between style and content.  There are no welldefined or agreedupon evaluation metrics for unpaired imagetoimage translation. So far, the success of image translation models has been based on subjective, qualitative visual comparison on a limited number of images. To address this problem, we propose a new framework for the quantitative evaluation of imagetoillustration models, where both content and style are taken into account using separate classifiers. In this new evaluation framework, our proposed model performs better than the current stateoftheart models on the illustrations dataset. Our code and pretrained models can be found at  https:github.comgiddyyuppganilla .