The Evolution of Generative AI Models: From GANs to Transformers

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Generative Adversarial Networks (GANs),
introduced by Ian Goodfellow and his colleagues
in 2014, marked a significant breakthrough in the
field of generative modeling. GANs operate on a
simple yet powerful principle two neural
networks, a generator and a discriminator, engage
in a competitive game. The generator attempts to
produce realistic data, such as images, while the
discriminator aims to distinguish between real
and generated data. Through this adversarial
process, GANs learn to generate increasingly
realistic samples.
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The success of GANs lies in their ability to
capture complex data distributions and generate
high-fidelity outputs. They have found
applications in diverse domains, from image
synthesis and style transfer to data augmentation
and image-to-image translation.
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In conclusion, the evolution of Generative AI
models from GANs to Transformers represents a
journey of continuous innovation and exploration.
GANs pioneered the adversarial training paradigm,
enabling realistic data generation, while
Transformers introduced a novel architecture that
excels in sequence-based tasks.
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