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in simple GANs, the discriminator has strong bias on what a "real" image looks like. as result, it teaches the generator to produce a single image, regardless of the initial input.
ive seen some research around minibatch discrimination, which makes sure features within various samples remain varied, and therefore avoids a single output.
I ran example_gan.py and I got mostly 1's and 7's. @mynameisvinn Could you elaborate a bit more if you know how to improve this? I don't get anything close to the picture show in this git.
I am using Keras 2 and the latest keras-adverserial code that you wrote for keras-2. @bstriner Could you help me debug this?
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