Table of Contents
What is a random input in Gan lab?
In GAN Lab, a random input is a 2D sample with a (x, y) value (drawn from a uniform or Gaussian distribution), and the output is also a 2D sample, but mapped into a different position, which is a fake sample. One way to visualize this mapping is using manifold [Olah, 2014]. The input space is represented as a uniform square grid.
What is a Gan generator?
As described earlier, the generator is a function that transforms a random input into a synthetic output. In GAN Lab, a random input is a 2D sample with a (x, y) value (drawn from a uniform or Gaussian distribution), and the output is also a 2D sample, but mapped into a different position, which is a fake sample.
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How do I start training the GAN model?
Let training begin. To start training the GAN model, click the play button () on the toolbar. Besides real samples from your chosen distribution, you’ll also see fake samples that are generated by the model. Fake samples’ positions continually updated as the training progresses.
How do I choose a probability distribution for Gan?
At top, you can choose a probability distribution for GAN to learn, which we visualize as a set of data samples. Once you choose one, we show them at two places: a smaller version in the model overview graph view on the left; and a larger version in the layered distributions view on the right.
How does a perfect Gan create fake samples?
A perfect GAN will create fake samples whose distribution is indistinguishable from that of the real samples. When that happens, in the layered distributions view, you will see the two distributions nicely overlap. Figure 2.
Who created the Gan lab?
Who developed GAN Lab? GAN Lab was created by Minsuk Kahng, Nikhil Thorat, Polo Chau, Fernanda Viégas, and Martin Wattenberg, which was the result of a research collaboration between Georgia Tech and Google Brain/ PAIR.