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@article{159511,
        author = {Gautam Gupta and Joshuva Jeemon and Supriya Mohite and Shubham Karande and Kirti Motwani},
        title = {Text to Image Generation},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {9},
        number = {12},
        pages = {1065-1069},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=159511},
        abstract = {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.},
        keywords = {Generator, Discriminator, Generative adversarial networks, Conditioning augmentation. },
        month = {},
        }