GANs are a hot topic of research today in the field of deep learning. Popularity has soared with this architecture style, with its ability to produce generative models that are typically hard to learn. There are a number of advantages to using this architecture: it generalizes with limited data, conceives new scenes from small datasets, and makes simulated data look more realistic.
For implementing GAN we used Keras Framework
𝗣𝗹𝗮𝘆𝗹𝗶𝘀𝘁: • Generative Adversarial Network-GAN(Ba...
𝗚𝗶𝘁𝗵𝘂𝗯 𝗻𝗼𝘁𝗲𝗯𝗼𝗼𝗸 𝗹𝗶𝗻𝗸: https://github.com/HelloJahid/Machine...
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