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[DL輪読会]A Style-Based Generator Architecture for Generative Adversarial Networks

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Published on

2019/01/25
Deep Learning JP:
http://deeplearning.jp/seminar-2/

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[DL輪読会]A Style-Based Generator Architecture for Generative Adversarial Networks

  1. 1. 1 DEEP LEARNING JP [DL Papers] http://deeplearning.jp/ A Style-Based Generator Architecture for Generative Adversarial Networks
  2. 2. • • ( ) ( • • • • • 2
  3. 3. • deb – gN G IG • de S Q • A :8 :1 1 :8 82 28: :8 10 , 0 : 8 aD P • V f i 3
  4. 4. • G , • e l l – l g r do • p e n – n g s t o • i a • , , , 4
  5. 5. • Ø 5
  6. 6. • 6 ) ) 12 ( 6 • 2 0 6 L. Gatys et al. “Image StyleTransfer Using Convolutional Neural Networks Leon”, CVPR, 2016
  7. 7. Content loss Style loss • G • – • – 7
  8. 8. • , – , • , 8
  9. 9. AdaIN • gaN H X I I Nc A • ]I ie • ) , N ., 1 , ( 3, 1 , 1 ., ( [ H X I ] d 9
  10. 10. AdaIN • – I B • – I B 10
  11. 11. AdaIN • – I B • – I B 11 どちらも, γとβは学習パラ メータ
  12. 12. AdaIN • , – N – Adc I – A a N e 12 X. Huang et al. “Arbitrary StyleTransfer in Real-time with Adaptive Instance Normalization”, 2017.
  13. 13. • , AN • N A A • I 13
  14. 14. Style based generator • N 2 41 I 2 • , I A 5 2 5 • 5 2 14
  15. 15. • N L S x mn h kQ r – A ) - – g iFdl P S I – A ) -S - – D - B S u – Se v pH – ( ty C E pH • F a , (( F a z G F a • ow ( – ( ) - F s w 15
  16. 16. style-based generator • - - , , A Ø I 16
  17. 17. mixing regularization • A 1 I • , 2 1 17 例) 異なる潜在変数 から生成したパラ メータを用いる
  18. 18. mixing regularization 18 •
  19. 19. mixing regularization • 648 , 6 :1 - 6 03 043 20 • ,4 6 6 :1 ) – • 20--1 6 :1 ( – • 03 6 :1 ( – Ø 6 :1 19 Styleをmix
  20. 20. • , – 20
  21. 21. disentanglement • , , , • G G A Ø , - Ø N 21
  22. 22. Perceptual path length • e a e o p • ] • e , o • n a e 4 , o l [ i 22 S. Laine. Feature-based metrics for exploring the latent space of generative models. ICLR workshop poster, 2018.
  23. 23. Perceptual path length • b e a c h d g • ( h d l • 2-) 2 ) , - , ) - , ) , W b , ) - ) i • 2 -) 2)( ) ) e n • W -) -, ) , ) - , 23
  24. 24. Perceptual path length • • - 24
  25. 25. Linear separability • , 2 • 2 , 25
  26. 26. Linear separability c ) s Va 23 1 34350 H do c t S H mt H , do ( V Y :. n – .) , r ) Va • S i V X V n | • S M 26
  27. 27. The FFHQ datase • ( ( U Ua )hg • -.0., /1 Lc e QR H U a • E 20C d A:7 4 AB 7 27
  28. 28. • g a y / o • t s i xd • z – p v – e : v – r h nz • r h l : z s • // ./ ) ( 28
  29. 29. 7 1 K L S 6L K . G LH BCL L H . G LCN N K C 3 L H T 2 . L K L S0F A 6L 7 GK 8KCGA ,HGNH LCHG 3 3 L H K 2 HGT , 4 GA L S CL 6L 7 GK CG LCF CLB ILCN 0GKL G 3H F CR LCHGT 6 2 CG S- L K F L C K H PI H CGA LB L GL KI H A G LCN FH KT 0,2 H KBHI IHKL 29

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