Generative Adversarial Networks for Data Augmentation
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Abstract
Data augmentation is a crucial technique in the realm of machine learning and computer vision, aimed at increasing the diversity and size of training datasets to improve the performance of various models. Generative Adversarial Networks (GANs) have gained prominence as a powerful tool for data augmentation. This paper explores the application of GANs in generating synthetic data samples that are indistinguishable from real data. We discuss the architecture of GANs, training strategies, and their potential impact on improving the robustness and generalization of machine learning models. Moreover, we present a comprehensive review of recent advancements and challenges in this area, shedding light on the potential of GANs for data augmentation across different domains.
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Chaitanya Krishna Suryadevara, “TOWARDS PERSONALIZED HEALTHCARE - AN INTELLIGENT MEDICATION RECOMMENDATION SYSTEM”, IEJRD - International Multidisciplinary Journal, vol. 5, no. 9, p. 16, Dec. 2020.
Suryadevara, Chaitanya Krishna, Predictive Modeling for Student Performance: Harnessing Machine Learning to Forecast Academic Marks (December 22, 2018). International Journal of Research in Engineering and Applied Sciences (IJREAS), Vol. 8 Issue 12, December-2018, Available at SSRN: https://ssrn.com/abstract=4591990
Suryadevara, Chaitanya Krishna, Unveiling Urban Mobility Patterns: A Comprehensive Analysis of Uber (December 21, 2019). International Journal of Engineering, Science and Mathematics, Vol. 8 Issue 12, December 2019, Available at SSRN: https://ssrn.com/abstract=4591998
Chaitanya Krishna Suryadevara. (2019). A NEW WAY OF PREDICTING THE LOAN APPROVAL PROCESS USING ML TECHNIQUES. International Journal of Innovations in Engineering Research and Technology, 6(12), 38–48. Retrieved from https://repo.ijiert.org/index.php/ijiert/article/view/3654
Chaitanya Krishna Suryadevara. (2020). GENERATING FREE IMAGES WITH OPENAI’S GENERATIVE MODELS. International Journal of Innovations in Engineering Research and Technology, 7(3), 49–56. Retrieved from https://repo.ijiert.org/index.php/ijiert/article/view/3653
Chaitanya Krishna Suryadevara. (2020). REAL-TIME FACE MASK DETECTION WITH COMPUTER VISION AND DEEP LEARNING: English. International Journal of Innovations in Engineering Research and Technology, 7(12), 254–259. Retrieved from https://repo.ijiert.org/index.php/ijiert/article/view/3184
Chaitanya Krishna Suryadevara. (2021). ENHANCING SAFETY: FACE MASK DETECTION USING COMPUTER VISION AND DEEP LEARNING. International Journal of Innovations in Engineering Research and Technology, 8(08), 224–229. Retrieved from https://repo.ijiert.org/index.php/ijiert/article/view/3672
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., ... & Bengio, Y. (2014). Generative adversarial nets. In Advances in neural information processing systems.
Radford, A., Metz, L., & Chintala, S. (2015). Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv preprint arXiv:1511.06434.
Arjovsky, M., Chintala, S., & Bottou, L. (2017). Wasserstein GAN. arXiv preprint arXiv:1701.07875.
Zhang, H., Xu, T., Li, H., Zhang, S., Huang, X., Wang, X., & Metaxas, D. N. (2017). StackGAN: Text to photo-realistic image synthesis with stacked generative adversarial networks. In Proceedings of the IEEE international conference on computer vision.
Antipov, G., Berrani, S. A., & Dugelay, J. L. (2017). Face aging with conditional generative adversarial networks. In Proceedings of the IEEE conference on computer vision and pattern recognition.