Comments (2)
theres no easy way to debug this issue.
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.
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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.
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Related Issues (20)
- 'AdversarialModel' object has no attribute '_feed_output_shapes' HOT 3
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- Error: _gru_ops.dll not found // tensorflow 1.6.0, python 3.6.1 HOT 2
- ValueError: Error when checking model input: the list of Numpy arrays that you are passing to your model is not the size the model expected. Expected to see 3 array(s), but instead got the following list of 2 arrays.
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- Non-image, AAE
- https://github.com/keras-team/keras/issues/6263#issuecomment-449274947
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