Text to Image Generation
Gautam Gupta, Joshuva Jeemon, Supriya Mohite, Shubham Karande, Kirti Motwani
Generator, Discriminator, Generative adversarial networks, Conditioning augmentation.
Machine Learning enables near-perfect algorithmic compositions. The proposed solution, Stacked Generative Adversarial Networks, generates photo-realistic images from text descriptions by decomposing the problem into manageable sub-problems through a sketch-refinement process. The Stage-I GAN sketches low-resolution images of the object's primitive shape and colors. The Stage-II GAN generates high-resolution images with photo-realistic details by rectifying defects in Stage-I results and adding compelling details with the refinement process. A Conditioning Augmentation technique improves diversity and stabilizes training. The proposed method achieves significant improvements in generating photo-realistic images conditioned on text descriptions.
Article Details
Unique Paper ID: 159511

Publication Volume & Issue: Volume 9, Issue 12

Page(s): 1065 - 1069
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