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View Code? Open in Web Editor NEWAn empirical study on evaluation metrics of generative adversarial networks.
Home Page: https://arxiv.org/abs/1806.07755
An empirical study on evaluation metrics of generative adversarial networks.
Home Page: https://arxiv.org/abs/1806.07755
The TF version of GAN metrics, e.g. IS, FID, are using 10000 samples, while sampleSize==2000 in the demo.
The setting of "sampleSize==10000" may cause out of memory problem, as dozens of GB mem is requested by "metrics.py".
Could the code used to reproduce the "sampleSize==10000" experiments as TF version does?
Thanks for your great work.
I'm wondring what is the real meaning of the function distance. at first I thought it was an Euclidean distance between two features spaces but when reading the code it is not . I also don't get it why Mxx != 0 ! the distance to itself should be always 0 otherwise its only a function name but not a real distance.
I hope somone could clarify this concept. I will be glad to have any documentation, paper or articles
Thnx
I noticed the line 428 of metric.py which compute FID in softmax space, the original FID should compute in conv space instead of softmax space, and I find no description about this.
I was going through your feature calculation for Inception_v3 in metrics.py and found out that you did not use the 'inception.Mixed_6e' block in the feature calculation. Can you comment on the reasons you chose to do that?
Hello, how did you get the fractional polyline diagram of pixel space and convolution space in your paper?How should I operate to get the same result as you?
How do you operate on changes in the number of false images contained in the overall dataset? I didn't find the relevant code?
Thank you very much for your help.
Hi,
In metrics.py
where you have defined the inception feature, inception.Mixed_6e is missing and this result is getting different outputs which is not comparable to other methods.
Is it suitable for all pictures? Tahnk you.
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