Generative Adversarial Networks (GANs)
Generative Adversarial Networks or GANs are a type of machine learning algorithm used for unsupervised learning. I use two neural networks competing with each other. One is the generator, and the other is a discriminator. The term adversarial is used in its name meaning that these two networks are pitted against each other in a zero-sum game.
A Generative Adversarial Network has two parts:
- The Generator tries to fool the discriminator by producing random noise samples based on the samples provided while training.
- The Discriminator tries to distinguish between the data produced by the generator and the actual data provided while training the model.
At the start of training, the Generator starts by producing absolute fake data that the discriminator can easily identify this is fake. As the training keeps on going the generator gets closer to producing output that can’t be easily distinguished by the Discriminator. Finally, If the training goes as planned the discriminator will lose the game and fail in distinguishing between the real and the fake output. As a result of this interactive competition between the generator and discriminator, both of the neural networks drive toward advancement. And becomes capable of generating realistic, high-quality images. GANs are highly versatile algorithms as they can be used in image synthesis, style transfer, etc.
How does an AI Model generate Images?
We all are living in an era of Artificial Intelligence and have felt its impact. There are numerous AI tools for various purposes ranging from Text Generation to image Generation to Video Generation to many more things. You must have used text-to-image models like Dall-E3, Stable Diffusion, MidJourney, etc. And it might be that you’re fascinated with their image-generation capabilities as they can generate realistic images of non-existent objects or can enhance existing images. They can convert your imagination into an image in a matter of seconds. But how?
In this article, we are going to explore how all these TTM models have this kind of imagination that can generate images that they’ve never seen.
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