Many image processing tasks can be formulated as translating images between two image domains, such as colorization, super-resolution, and conditional image synthesis. Inmost of these tasks, an input image may correspond to multiple outputs. However, current existing approaches only show minorstochasticity of the outputs. In this paper, we present a novel approach to synthesize diverse realistic images corresponding to a semantic layout. We introduce a diversity loss objective, which maximizes the distance between synthesized image pairs and relates the input noise to the semantic segments in the synthesized images. Thus, our approach can not only produce multiple diverse images but also allow users to manipulate the output images by adjusting the noise manually. Experimental results show that images synthesized by our approach are diverse than that of the current existing works and equipping our diversity loss does not degrade the reality of the base networks. Moreover,our approach can be applied to unpaired datasets.
To View the Base Paper Abstract Contents
Now it is Your Time to Shine.
Great careers Start Here.
We Guide you to Every Step
Success! You're Awesome
Thank you for filling out your information!
We’ve sent you an email with your Final Year Project PPT file download link at the email address you provided. Please enjoy, and let us know if there’s anything else we can help you with.
To know more details Call 900 31 31 555
The WISEN Team