Date of Publication :31st October 2023

Abstract: Generative Adversarial Networks (GANs) have emerged as a powerful framework for generating realistic data through adversarial training. This abstract introduces the concept of GANs and demonstrates their implementation using the TensorFlow framework. GANs, short for Generative Adversarial Networks, are comprised of a pair of neural networks: a generator and a discriminator. These networks participate in a two-player minimax game. The primary objective of the generator is to produce synthetic data that closely mimics real data, whereas the discriminator is tasked with distinguishing authentic data from the counterfeit counterpart. This dynamic interplay between generator and discriminator leads to the refinement and enhancement of the generated data over time. Through iterative training, GANs learn to refine the generator's output, leading to the generation of increasingly convincing data samples. This abstract provides an overview of GAN architecture, training process, and evaluation methods, along with a code example utilizing TensorFlow to create a basic GAN for generating images. By understanding the core principles of GANs and their implementation in TensorFlow, researchers and developers can harness their potential for various applications, including image synthesis, style transfer, and data augmentation.

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