StyleGAN Explained

In this video, I have explained what are Style GANs and what is the difference between the GAN and StyleGAN.

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Generative Adversarial Networks, or GANs for short, are effective at generating large high-quality images.

Most improvement has been made to discriminator models in an effort to train more effective generator models, although less effort has been put into improving the generator models.

The Style Generative Adversarial Network, or StyleGAN for short, is an extension to the GAN architecture that proposes large changes to the generator model, including the use of a mapping network to map points in latent space to an intermediate latent space, the use of the intermediate latent space to control style at each point in the generator model, and the introduction to noise as a source of variation at each point in the generator model.

The resulting model is capable not only of generating impressively photorealistic high-quality photos of faces, but also offers control over the style of the generated image at different levels of detail through varying the style vectors and noise.

The StyleGAN is an extension of the progressive growing GAN that is an approach for training generator models capable of synthesizing very large high-quality images via the incremental expansion of both discriminator and generator models from small to large images during the training process.

In addition to the incremental growing of the models during training, the style GAN changes the architecture of the generator significantly.

The StyleGAN generator no longer takes a point from the latent space as input; instead, there are two new sources of randomness used to generate a synthetic image: a standalone mapping network and noise layers.



















































































































































































































































































































































































































































































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