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|+ '''T-SNE algorithm results for ''missing'' class synthesized images'''
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|[[File:Missing 512x512.png|thumb|400x400px350x350px|512 × 512 resolution ''full'' images]]
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|[[File:Missing 256x256.png|thumb|400x400px350x350px|256 × 256 resolution ''full'' images]]
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|[[File:Missing upper lower 256x256.png|thumb|400x400px350x350px|256 × 256 resolution ''upper'' and ''lower'' region images]]
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{| style="background:transparent; color:black" border="0" align="center" cellpadding="10px" cellspacing="0px" height="550" valign="bottom"
|- align="center"
|+ '''T-SNE algorithm results for ''tombstoning'' class synthesized images'''
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|[[File:Tombstoning 512x512.png|thumb|350x350px|512 × 512 resolution ''full'' images]]
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|[[File:Tombstoning 256x256.png|thumb|350x350px|256 × 256 resolution ''full'' images]]
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|[[File:Tombstoning upper lower 256x256.png|thumb|350x350px|256 × 256 resolution ''upper'' and ''lower'' region images]]
|}
==Useful links==
*Tero Karras, Timo Aila, Samuli Laine, Jaakko Lehtinen, [https://arxiv.org/abs/1710.10196 ''Progressive Growing of GANs for Improved Quality, Stability, and Variation''], February 2018.
* Ishaan Gulrajani, Faruk Ahmed, Martin Arjovsky, Vincent Dumoulin, Aaron Courville, [https://arxiv.org/abs/1704.00028 ''Improved Training of Wasserstein GANs''], December 2017.
