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[D] Monitor the balance between the training of the discriminator and generator in GANs

I am looking for a way to monitor the balance between the training of the discriminator (D) and the generator (G) in GANs. I am aware of numerous heuristic as well as non-heuristic ways to stabilize the training (minibatch discrimination, label smoothing, crippling the discriminator, GP and many others), but I haven’t found a method that would ideally provide a scalar value denoting the balance between training.

The obvious questions is to compare losses of D and G, or their gradients. However, this is extremely noisy and I believe that there should be a better/different way to measure it out there. A different take on it is to consider for example FID as the scalar value denoting the balance. If you know of any methods, let me know!

submitted by /u/mw_molino
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Toronto AI is a social and collaborative hub to unite AI innovators of Toronto and surrounding areas. We explore AI technologies in digital art and music, healthcare, marketing, fintech, vr, robotics and more. Toronto AI was founded by Dave MacDonald and Patrick O'Mara.