Automated Voice-to-Image Generation Using Generative Adversarial Networks in Machine Learning-Reference-Cited by-同舟云学术

Automated Voice-to-Image Generation Using Generative Adversarial Networks in Machine Learning

Published:2023 Issue: Volume:430 Page:01065
ISSN:2267-1242
Container-title:E3S Web of Conferences
language:
Short-container-title:E3S Web Conf.

Author:

Yeluri Lakshmi Prasanna,Ramesh G.,Vijayalata Y.,Shareef Khaja,Chamola Shailesh,Gundavarapu Mallikarjuna Rao

Abstract

Creating visuals from words may appear to be a complex process, but it is achievable with today’s technological advancements in Information Systems. Naturally, all the human-centric actions and assumptions may lead to visualization using Artificial Intelligence. In today’s Information Systems technological world, any item or a thing can be best described in pictorial form as a human person. Our paper aims to focus on providing machines with this intelligence. To complete this challenge, we used Natural Language Processing with Deep Learning. Our primary focus is on Generative Adversarial Networks. GANs will generate data based on word labels that are provided. NLP is also important since it helps to translate the provided speech into embedding vectors that the model can use. Our study is on the CUB dataset, which comprises bird photos. In today’s world, there are text-to-image generating models accessible. The authors investigated all of them, extending text-to-image generation to voice-to-image generation.

Publisher

EDP Sciences

Subject

General Medicine

Link

https://www.e3s-conferences.org/10.1051/e3sconf/202343001065/pdf

Reference28 articles.

1. Goodfellow I. J., Pouget-Abadie J., Mirza M., Xu B., WardeFarley D., Ozair S., Courville A. C., and Bengio Y.. Generative adversarial nets. In NIPS, (2014).

2. Salimans T., Goodfellow I. J., Zaremba W., Cheung V., Radford A., and Chen X.. Improved techniques for training gans. In NIPS, (2016).

3. Gregor K., Danihelka I., Graves A., Rezende D. J., and Wierstra D.. DRAW: A recurrent neural network for image generation. In ICML, (2015).

4. Isola P., Zhu J.-Y., Zhou T., and Efros A. A.. Image-to-image translation with conditional adversarial networks. In CVPR, (2017).

5. Ledig C., Theis L., Huszar F., Caballero J., Aitken A., Tejani A., Totz J., Wang Z., and Shi W.. Photo-realistic single image super resolution using a generative adversarial network. In CVPR, (2017).