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View Code? Open in Web Editor NEWArtFID: Quantitative Evaluation of Neural Style Transfer
ArtFID: Quantitative Evaluation of Neural Style Transfer
Thanks for your excellent paper and well-organized codes. I have few questions about the experiment settings in the paper though.
In section 4.1 of the paper, you said that "Style images are sampled from the WikiArt dataset [70] and the BAM dataset [78]." and "we compute the ArtFID with samples of 50k images." and "For each style transfer method, the ArtFID is computed with 5 different samples containing 50k images each.".
I have few questions about the experiment settings.
Thanks for your excellent paper again and I am hoping for your response!
Hi experts,
The e.q.2 is sum of LPIPS and FID_infinite.
LPIPS between content and stylized images more higher is better, but opposite on e.q.2.
ref:
https://arxiv.org/pdf/2201.12543.pdf
https://arxiv.org/pdf/2208.00921.pdf
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9244092
https://arxiv.org/pdf/2006.01431.pdf
FID between style and stylized images is not good for distribution of holistic statistic.
something like context and structure difference from style and stylized images.
for example: stylized animal dataset and original style image from Monet
SIFID from SinGAN in which use single images pair distribution
https://arxiv.org/pdf/1905.01164.pdf
IS_infinite: The Inception score is the expectation of KL divergence distance between two sets of generated images
https://arxiv.org/pdf/2006.01431.pdf
Do you have been some experiments about these two metric functions?
Would it be possible to share the code used for training the inception network? It would be very helpful!
My e-mail address is [email protected]
Thanks very much!
Would it be possible to share the code used for training the Inception network? It would be really helpful.
Thank you in advance, and thank you for your contribution.
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