Hi,
I did some experiments reimplementing a DCGAN like for generating MNIST digits (full code at GitHub - jeremyfix/gan_experiments 1). It seems to me the generated digits are pretty good (see https://raw.githubusercontent.com/jeremyfix/gan_experiments/main/mnist.gif)
And indeed, even interpolating in the latent space seems to produce a large variety of digits (to produce the plot below, I generated 3 random noises and then interpolated the noise to feed in the generator) (see the github page. I cannot put more than 1 image or more than 2 links)
But, when it turns to plot the losses, these seems to me unexpected.
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They are all consistent : a high generator loss (that I want to be minimized actually…), almost 90% accuracy for the discriminator. This indicates the discriminator is able to distinguish the fakes from the reals 90 % of the time but when I see the digits the generator generates, I see no obvious things and it seems to generate a variety of shapes which seems to indicate this is not a mode collapse.
Do you have any idea on this behavior ?