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Generative Adversarial Networks (GANs) have evolved significantly since their inception, with key developments including DCGAN, WGAN, and StyleGAN, which have enhanced image realism and stability. Their applications range from image synthesis and data augmentation to deepfakes and super-resolution, while challenges such as mode collapse and vanishing gradients persist. Ethical concerns surrounding misinformation, identity theft, and bias highlight the need for responsible development and regulation in the u

Author

arvindkverma2007

Language

EN