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2021, Vol: 8, Issue: 1
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Research Article 


TEXT TO IMAGE GENERATION USING HYBRID ATTENTION GENERATIVE ADVERSARIAL NETWORK

Bhavya Bordia, Shaswat Patel, Biju R Mohan, Supreeth G.

Abstract
Visualization is essential because it makes learning, creating, planning much easier, and to visualize we need a description that provides meaning to the visual. Due to the advancement in Machine Learning Algorithms, they can help in translating the descriptions to visuals. Generative Adversarial Networks (also known as GANs) can be used to create a set of images from the text which are a form of descriptions. Generative models algorithms come under unsupervised machine learning. GANs have many applications and they can be used in visualizing and creating models according to the need just by describing it. They can be used in architect planning, home designing. They can also be used in creating animations, rather it can make the process faster and hence can be used to make virtual games. GANs can be used also be used in the apparel industry and advertising sectors. But these all are the advance use of the algorithms which may be implemented in the future. Our project aim is just to create a set of images from captions given by users in text format, which are semantically matching with the text and seems too realistic. We will be using different generative models for this purpose.

Key words: Generative Adversarial Networks, Text-to- Image Generation, Image Re-description, Unsupervised Machine Learning


 
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How to Cite this Article
Pubmed Style

Bhavya Bordia, Shaswat Patel, Biju R Mohan, Supreeth G. TEXT TO IMAGE GENERATION USING HYBRID ATTENTION GENERATIVE ADVERSARIAL NETWORK. JCR. 2020; 7(15): 6068-6075. doi:10.31838/jcr.07.15.776

Web Style

Bhavya Bordia, Shaswat Patel, Biju R Mohan, Supreeth G. TEXT TO IMAGE GENERATION USING HYBRID ATTENTION GENERATIVE ADVERSARIAL NETWORK. http://www.jcreview.com/?mno=7937 [Access: January 29, 2021]. doi:10.31838/jcr.07.15.776

AMA (American Medical Association) Style

Bhavya Bordia, Shaswat Patel, Biju R Mohan, Supreeth G. TEXT TO IMAGE GENERATION USING HYBRID ATTENTION GENERATIVE ADVERSARIAL NETWORK. JCR. 2020; 7(15): 6068-6075. doi:10.31838/jcr.07.15.776


Vancouver/ICMJE Style

Bhavya Bordia, Shaswat Patel, Biju R Mohan, Supreeth G. TEXT TO IMAGE GENERATION USING HYBRID ATTENTION GENERATIVE ADVERSARIAL NETWORK. JCR. (2020), [cited January 29, 2021]; 7(15): 6068-6075. doi:10.31838/jcr.07.15.776


Harvard Style

Bhavya Bordia, Shaswat Patel, Biju R Mohan, Supreeth G (2020) TEXT TO IMAGE GENERATION USING HYBRID ATTENTION GENERATIVE ADVERSARIAL NETWORK. JCR, 7 (15), 6068-6075. doi:10.31838/jcr.07.15.776


Turabian Style

Bhavya Bordia, Shaswat Patel, Biju R Mohan, Supreeth G. 2020. TEXT TO IMAGE GENERATION USING HYBRID ATTENTION GENERATIVE ADVERSARIAL NETWORK. Journal of Critical Reviews, 7 (15), 6068-6075. doi:10.31838/jcr.07.15.776


Chicago Style

Bhavya Bordia, Shaswat Patel, Biju R Mohan, Supreeth G. "TEXT TO IMAGE GENERATION USING HYBRID ATTENTION GENERATIVE ADVERSARIAL NETWORK." Journal of Critical Reviews 7 (2020), 6068-6075. doi:10.31838/jcr.07.15.776


MLA (The Modern Language Association) Style

Bhavya Bordia, Shaswat Patel, Biju R Mohan, Supreeth G. "TEXT TO IMAGE GENERATION USING HYBRID ATTENTION GENERATIVE ADVERSARIAL NETWORK." Journal of Critical Reviews 7.15 (2020), 6068-6075. Print. doi:10.31838/jcr.07.15.776


APA (American Psychological Association) Style

Bhavya Bordia, Shaswat Patel, Biju R Mohan, Supreeth G (2020) TEXT TO IMAGE GENERATION USING HYBRID ATTENTION GENERATIVE ADVERSARIAL NETWORK. Journal of Critical Reviews, 7 (15), 6068-6075. doi:10.31838/jcr.07.15.776


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