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I tried to understand how VQ-GAN works, but unfortunately I have not understood it. I tried to read some articles about it and watch a video. I believe a good and simple article will help me. You helped me with transformers. You gave me wonderful link. I know what CNNs, GANs, TRANSFORMERSs, and CLIP are. I somehow understood a bit how VQ-VAE works, but still do not understand some things about it. But I cannot understand how VQ-GAN works. Please, help me to understand it. Thanks.

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  • $\begingroup$ It will be better if you walkthrough the paper and mask more specific questions about where you are losing track, that way I could provide clearer answers. $\endgroup$ Sep 1, 2021 at 14:16

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GANs are a type of deep learning model that consist of two parts: a generator network that produces fake images, and a discriminator network that tries to distinguish the fake images from real ones. The generator and discriminator compete with each other, with the generator trying to produce images that the discriminator cannot tell apart from real images, and the discriminator trying to get better at telling them apart.

VQ-GAN is a type of generative adversarial network (GAN) that uses a vector quantization (VQ) mechanism to improve the quality and diversity of the generated images.

In a VQ-GAN, the generator network uses a vector quantization (VQ) layer to quantize its output into a set of discrete vectors. This has the effect of forcing the generator to produce images that are more similar to the training data, because the quantization ensures that the generated images are a "nearest neighbor" of the training images in the discrete code space. In other words, the VQ layer encourages the generator to produce images that are similar to ones it has seen before, rather than generating completely novel images.

The VQ-GAN architecture also includes a learned "encoder" network that maps real images to the same discrete code space as the generator. This allows the discriminator to learn a distance metric in the discrete code space, which allows it to better distinguish between real and fake images.

The VQ-GAN architecture is similar to that of a VQ-VAE, which is a type of generative model that also uses a VQ layer to learn a discrete representation of the data. However, unlike a VQ-VAE, a VQ-GAN uses a discriminator network to guide the learning process and to provide feedback to the generator on the quality of its generated samples. This allows the VQ-GAN to learn a more effective generative model than a VQ-VAE, which only uses the reconstruction error of the VQ layer as a learning signal.

Overall, VQ-GAN combines the strengths of GANs and VQ-VAE (vector quantized variational autoencoders) to produce high-quality images that are similar to the training data. It is a relatively recent development in the field of deep learning, and there is still ongoing research into the best ways to design and train VQ-GANs. To learn more about VQ-GAN, you can read the original paper by Oord et al., which describes the architecture and provides some experimental results.


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