Abstract
Image-to-image translation is the task of translating images between domains while maintaining the identities of the images. Generative Adversarial Networks (GANs), and in particular conditional GANs have recently shown incredible success in image-to-image translation and semantic manipulation. Such methods require paired data, meaning that an image must have ground-truth translations across domains. Cycle-consistent GANs solve this problem by using unpaired data. Such methods work well for translations that involve color and texture changes but fail when shape changes are required. This paper firstly analyzes the trade-offs between the cycle-consistency importance and the necessary shape changes required for natural looking imagery. We then propose computationally simple architectural and loss changes to allow the model to perform color, texture, and shape changes as required. The results demonstrate improved translations between domains that require shape changes. We additionally show how the embeddings learned by our model learn interesting and useful attention/segmentation information about the translated images.
Published Version
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