Text to Image Generation Using Machine Learning
A method called text-to-image involves creating images automatically from provided written descriptions. It contributes significantly to artificial intelligence by tackling the problem of integrating textual and visual input. One of the usefulness of automatic picture synthesis is the generation...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English English |
| Published: |
INTI International University
2024
|
| Subjects: | |
| Online Access: | http://eprints.intimal.edu.my/2067/1/jods2024_60.pdf http://eprints.intimal.edu.my/2067/2/608 http://eprints.intimal.edu.my/2067/ http://ipublishing.intimal.edu.my/jods.html |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | A method called text-to-image involves creating images automatically from provided written
descriptions. It contributes significantly to artificial intelligence by tackling the problem of
integrating textual and visual input. One of the usefulness of automatic picture synthesis is the
generation of images using conditional generative models. For this, Generative Adversarial
Networks (GANs) are frequently employed. Using GANs, recent developments in the sector have
made significant progress. An outstanding illustration of deep learning's potential is the
transformation of text into images. It is difficult to create a text-to-image synthesis system that
consistently creates realistic graphics based on predetermined criteria. Many of the existing
algorithms in this field struggle to produce visuals that precisely match the given text. In order to
solve this issue, we carried out a research work where we concentrated on developing the
generative adversarial network (GAN), a deep learning-based architecture. The aim of this
research work is to create a system that allows you to generate images that are semantically
consistent. |
|---|