Generative Adversarial Networks for Image Synthesis: Analyzing generative adversarial networks (GANs) for image synthesis tasks, including generating realistic images from noise or text
Keywords:
Generative Adversarial Networks, GANs, Image SynthesisAbstract
Generative Adversarial Networks (GANs) have emerged as a powerful framework for generating realistic images, offering promising results in various image synthesis tasks. This paper provides a comprehensive analysis of GANs for image synthesis, focusing on their architecture, training process, and applications. We explore the evolution of GANs, from the original formulation to recent advances, including conditional GANs and progressive GANs. Additionally, we discuss key challenges and future directions in GAN research for image synthesis.
Downloads
References
K. Joel Prabhod, “ASSESSING THE ROLE OF MACHINE LEARNING AND COMPUTER VISION IN IMAGE PROCESSING,” International Journal of Innovative Research in Technology, vol. 8, no. 3, pp. 195–199, Aug. 2021, [Online]. Available: https://ijirt.org/Article?manuscript=152346
Sadhu, Amith Kumar Reddy, and Ashok Kumar Reddy Sadhu. "Fortifying the Frontier: A Critical Examination of Best Practices, Emerging Trends, and Access Management Paradigms in Securing the Expanding Internet of Things (IoT) Network." Journal of Science & Technology 1.1 (2020): 171-195.
Tatineni, Sumanth, and Anjali Rodwal. “Leveraging AI for Seamless Integration of DevOps and MLOps: Techniques for Automated Testing, Continuous Delivery, and Model Governance”. Journal of Machine Learning in Pharmaceutical Research, vol. 2, no. 2, Sept. 2022, pp. 9-41, https://pharmapub.org/index.php/jmlpr/article/view/17.
Pulimamidi, Rahul. "Leveraging IoT Devices for Improved Healthcare Accessibility in Remote Areas: An Exploration of Emerging Trends." Internet of Things and Edge Computing Journal 2.1 (2022): 20-30.
Gudala, Leeladhar, et al. "Leveraging Biometric Authentication and Blockchain Technology for Enhanced Security in Identity and Access Management Systems." Journal of Artificial Intelligence Research 2.2 (2022): 21-50.
Sadhu, Ashok Kumar Reddy, and Amith Kumar Reddy. "Exploiting the Power of Machine Learning for Proactive Anomaly Detection and Threat Mitigation in the Burgeoning Landscape of Internet of Things (IoT) Networks." Distributed Learning and Broad Applications in Scientific Research 4 (2018): 30-58.
Tatineni, Sumanth, and Venkat Raviteja Boppana. "AI-Powered DevOps and MLOps Frameworks: Enhancing Collaboration, Automation, and Scalability in Machine Learning Pipelines." Journal of Artificial Intelligence Research and Applications 1.2 (2021): 58-88.
