A Survey on Face Generation Using Deep Learning

Authors

  • Dr. S. T. Shirkande  Department of Computer Engineering, SBPCOE, Indapur, Maharashtra, India
  • Mane Deshmukh Pradnya  Department of Computer Engineering, SBPCOE, Indapur, Maharashtra, India
  • Mane Shruti  Department of Computer Engineering, SBPCOE, Indapur, Maharashtra, India
  • Rakhunde Abhilasha  Department of Computer Engineering, SBPCOE, Indapur, Maharashtra, India
  • Waghmare Vaibhavi  Department of Computer Engineering, SBPCOE, Indapur, Maharashtra, India

Keywords:

Deep learning frameworks (e.g., TensorFlow, PyTorch), Image-to-image translation, Image synthesis. Unsupervised learning

Abstract

The field of deep learning has witnessed remarkable advancements in the generation of realistic and high-quality human faces. This abstract provides an overview of the techniques and methodologies employed in face generation using deep learning models. Face generation using deep learning primarily relies on Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs). These models have demonstrated the ability to synthesize lifelike faces, presenting a wide range of applications in computer vision, entertainment, and beyond.

References

  1. Image-to-image Translation with Conditional Adversarial Neworks (Pix2Pix) by Isola et al.(2016)
  2. CycleGAN: Unpaired Image-to-image Translation using Cycle-Consistent Adversarial Networks by Zhu et al.(2017)
  3. Progressive Growing of GANs for Improved Quality, Stability, and Variation by Karras et al.(2018)
  4. Star GAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-image Translation by Choi et al.(2018)
  5. SPADE: Semantic Image Synthesis with Spatially-Adaptive Normalization by Park et al.(2019)
  6. MUNIT: Multidimensional Unsupervised Image-to-image Translation by Huang et al.(2018)

IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

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Published

2023-10-30

Issue

Volume 9, Issue 10, September-October-2023

Section

Research Articles

License

Copyright (c) IJSRCSEIT

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Dr. S. T. Shirkande, Mane Deshmukh Pradnya, Mane Shruti, Rakhunde Abhilasha, Waghmare Vaibhavi, " A Survey on Face Generation Using Deep Learning" International Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 9, Issue 10, pp.94-97, September-October-2023.