GAN Implementation
Let's look at the implementation of the simple GAN based on the MNIST dataset.
Note
You can find the source code via the following Link. If you want to run the code or even change some components, you can copy the notebook and work with the copy.
Why does training take so long?
We need to train GANs for many more epochs compared to VAEs and their modifications because GAN training involves a complex adversarial process between the generator and discriminator, leading to potential instability and slow convergence.
Additionally, finding the equilibrium where the generator produces realistic data and the discriminator accurately distinguishes between real and fake data requires extensive and careful tuning over many epochs.
However, it's important to note that GANs can generate much more complex images than VAEs, making the lengthy training process worthwhile.
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Let's look at the implementation of the simple GAN based on the MNIST dataset.
Note
You can find the source code via the following Link. If you want to run the code or even change some components, you can copy the notebook and work with the copy.
Why does training take so long?
We need to train GANs for many more epochs compared to VAEs and their modifications because GAN training involves a complex adversarial process between the generator and discriminator, leading to potential instability and slow convergence.
Additionally, finding the equilibrium where the generator produces realistic data and the discriminator accurately distinguishes between real and fake data requires extensive and careful tuning over many epochs.
However, it's important to note that GANs can generate much more complex images than VAEs, making the lengthy training process worthwhile.
Tack för dina kommentarer!
