Inception generators for generative adversarial networks
Keywords:
Inception module, generative adversarial networks, image to image translation, Computer Vision, Machine Learning, Deep learningAbstract
Image-to-image translation aims at learning a mapping between two different picture styles. The style and texture of one image can be transferred to another. It is possible to do paired and unpaired image-to-image translations, both of which have a specified goal translation to aim for translating images. Using generative adversarial networks, we offer an overview of different generator designs for unpaired image-to-image translation and paired image-to-image translation. The Residual blocks or U-net architecture are often employed in generators of generative adversarial networks for image-to-image translation. In this research paper, we propose the use of an Inception module and their evaluation in unpaired image-to-image translation tasks by translating photos to artist images and also with unpaired data for converting Maps to aerial images.
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