Variational Auto Encoder入門

https://twitter.com/0hnishi
https://dena.ai/work7/

Variational Auto Encoder入門+ 教師なし学習∩deep learning∩生成モデルで特徴量作成

VAEなんとなく聞いたことあるけどよくは知らないくらいの人向け

Transcript

1. Copyright (C) 2019 DeNA Co.,Ltd. All Rights Reserved. Copyright (C) 2019 DeNA Co.,Ltd. All Rights Reserved. Variational Auto Encoder + 
   ∩deep learning∩    March 15, 2019 Katsunori Ohnishi DeNA Co., Ltd. 1
2. Copyright (C) 2019 DeNA Co.,Ltd. All Rights Reserved.
    n  n Unsupervised feature learning with deep generative model  Variational Auto-Encoder  Adversarial feature learning n 
    2
3. Copyright (C) 2019 DeNA Co.,Ltd. All Rights Reserved.
    n +> TS ( ) U Twitter: @0hnishi , speakerdeck: https://speakerdeck.com/katsunoriohnishi n 6P U 2014,4/-2017,9/: B4~M2.509+7Computer VisionGH KAM • 3L (8QC@): CVPR16 (spotlight oral), ACMMM16, AAAI18 (oral) U RN: http://katsunoriohnishi.github.io/ U 2017,10/-<B: DeNA AI!%)1 • DEDeNA(!#
   "$
   ):4Computer Vision/Data Science?5 '* & 2.= U → https://dena.ai/work7/ U JI-;OF  3
4. Copyright (C) 2019 DeNA Co.,Ltd. All Rights Reserved. 
    n 26 +7∩deep learning∩&.# $3951.!" 4 (-4%,*() 26 1. 395: >< Kaggle8)%  VAE
   /'0   0
5. Copyright (C) 2019 DeNA Co.,Ltd. All Rights Reserved. 
    n 8<0=∩deep learning∩+3'$(9?;73%"!#& 5 ( 2:*1/-) 8<$ 73  9?; @>< Kaggle>.*  VAE5,6  6 9)4 
6. Copyright (C) 2019 DeNA Co.,Ltd. All Rights Reserved. Auto-encoder n  encoder-decoder &"  6 $' loss ! Mean Squared Error(MSE) Self-supervised learning % #  https://hackernoon.com/a-deep-convolutional-denoising-autoencoder-for-image-classification-26c777d3b88e
7. Copyright (C) 2019 DeNA Co.,Ltd. All Rights Reserved. Auto-encoder
    7 Output!mask   segmentation “Auto” encoder-decoder'%)MN
   A> Segmentation Auto-encoder.48O encoder!=:K *G(&) %$→CG(&) "#3
     Pre-training MSE<P6BV!1; 8O  NoiseF BV!2 noiseE R27H?I/noise0 Noise & +5T27%$!LDJ@?QU   Bottleneck layer!9, -S6   [V. Badrinarayanan+, CVPR15] https://towardsdatascience.com/applied-deep-learning-part-3-autoencoders-1c083af4d798 [X. Zhan+, AAAI18]
8. Copyright (C) 2019 DeNA Co.,Ltd. All Rights Reserved. Auto-encoder
    n Pre-training: C
   7%59 C Test $∩ train  … 5> n Noise=; 0  n Anomaly detection18.?*6 n &(A<3)/',)!+/ C MSE 4B#8. "
   @&-2!  8
9. Copyright (C) 2019 DeNA Co.,Ltd. All Rights Reserved. Variational Auto-encoder 9 
   Auto-encoder Variational Auto-encoder       
   
10. Copyright (C) 2019 DeNA Co.,Ltd. All Rights Reserved. 
     n $("!#.*"!#- 2  1)encoder-decoder$("!# +0 $(   %,&.*"!# '/"!#  10 https://stanford.edu/~shervine/teaching/cs-229/cheatsheet-supervised-learning
11. Copyright (C) 2019 DeNA Co.,Ltd. All Rights Reserved. 
     n VAE ( #$'!   ( 
    &   z  %" 11
12. Copyright (C) 2019 DeNA Co.,Ltd. All Rights Reserved. Variational Auto-encoder n     12  https://qiita.com/kenmatsu4/i tems/b029d697e9995d93aa24 
13. Copyright (C) 2019 DeNA Co.,Ltd. All Rights Reserved. Variational Auto-encoder n      13      https://qiita.com/kenmatsu4/i tems/b029d697e9995d93aa24
14. Copyright (C) 2019 DeNA Co.,Ltd. All Rights Reserved. Variational Auto-encoder n 5,-#"3  14 /+45,0.   *! +4&!1' 5,0( +4 2 $%)1' https://hackernoon.com/latent-space-visualization-deep-learning-bits-2-bd09a46920df
15. Copyright (C) 2019 DeNA Co.,Ltd. All Rights Reserved. Variational Auto-encoder n Q:VAE#!=-0$2":6  ? A: ' 15 ()/":) &4":713.5>+":     VAE#!=-.*→output;*9,"713.5  8%< )/":( Tutorial on Variational Autoencoders [C. Doersch, arxiv15]
16. Copyright (C) 2019 DeNA Co.,Ltd. All Rights Reserved. Variational Auto-encoder n  predict/train         16
17. Copyright (C) 2019 DeNA Co.,Ltd. All Rights Reserved. Variational Auto-encoder n Forward"  !  17 ! " Σ " z Input X output Y Sample $ from % ! " , Σ "  z  #  
    backward
    
18. Copyright (C) 2019 DeNA Co.,Ltd. All Rights Reserved. Variational Auto-encoder n    18 ! " Σ " z Input X output Y Sample $ from % Ο, Ι ∗ + Forward Backward
19. Copyright (C) 2019 DeNA Co.,Ltd. All Rights Reserved. Variational Auto-encoder n Loss 19 ! " Σ " z Input X output Y Sample $ from % Ο, Ι ∗ + + − " - ./ %(!("), 2("))||% Ο, Ι KL !* q(z|X) &X $,6'logp(X|z) +/0 − (%&, *KL ! -.#2 1'4  *5') "30
20. Copyright (C) 2019 DeNA Co.,Ltd. All Rights Reserved. Variational Auto-encoder n 7459JG;? L Q: -/'.$+'!# … L A: MSE=E!  20
    DF,/0'> H3<9β"  KL)$,&%0' q(z|X) 5 
    *(X 29K6logp(X|z) 8@C − 7459 KL)$,&%0' A:IB (e.g. MSE) c β # − $ % A:*(:*( 6 →1 1: *( 8@
21. Copyright (C) 2019 DeNA Co.,Ltd. All Rights Reserved. Variational Auto-encoder n      ! /    • https://arxiv.org/pdf/1606.05908.pdf • https://qiita.com/kenmatsu4/items/b029d697e9995d93aa24 21 − 
22. Copyright (C) 2019 DeNA Co.,Ltd. All Rights Reserved. Variational Auto-encoder n   22 
     https://qiita.com/kenmatsu4/i tems/b029d697e9995d93aa24
23. Copyright (C) 2019 DeNA Co.,Ltd. All Rights Reserved. Variational Auto-encoder n :     
      23 ! " Σ " z Input X output Y Sample $ from % Ο, Ι ∗ +  c
24. Copyright (C) 2019 DeNA Co.,Ltd. All Rights Reserved. Variational Auto-encoder n  +36 AEVAE'. 7 5,-)$1/%  24 AE!4 5,-)$1  #0*2&( " VAE https://qiita.com/kenmatsu4/i tems/b029d697e9995d93aa24
25. Copyright (C) 2019 DeNA Co.,Ltd. All Rights Reserved. Variational Auto-encoder n :>4   B */DL&%&'15example- • Chainer: https://github.com/chainer/chainer/tree/master/examples/vae • Pytorch: https://github.com/pytorch/examples/tree/master/vae • Keras: https://github.com/keras-team/keras/blob/master/examples/variational_autoencoder.py B Tips • " !)+,=3< • A67.(0?  #$&2 • 0?@9  8; 25
26. Copyright (C) 2019 DeNA Co.,Ltd. All Rights Reserved. Variational Auto-encoder n $,(+#
    &2018%*"&(*%)"'%A! … K −"#$ −β    26 pytorch [Kingma+, ICLR14]     J=?/ 7>-G: 9;H@ :;H .6    CB< F https://github.com/pytorch/examples/blob/master/vae/main.py e.g.) D9IE 543 
27. Copyright (C) 2019 DeNA Co.,Ltd. All Rights Reserved. GAN
     n VAE     MSE Loss    GAN     27 https://skymind.ai/wiki/generative-adversarial-network-gan
28. Copyright (C) 2019 DeNA Co.,Ltd. All Rights Reserved. GAN
     n Discriminator#+1,3/ )- 5 "$( %'  28  real/fake0*  discriminator !24,3/& . ,3/ UCF101 Chance 0.9 unsupervised VGAN [C. Vondrick+, NIPS16] 36.7 FTGAN [K. Ohnishi+, AAAI18] 60.9 supervised Two-stream [K. Simonyan+, NIPS14] 88.0 I3D [J. Carreira+ CVPR17] 98.0 CIFAR10 Chance 10.0 unsupervised DCGAN [A. Radford+, ICLR16] 82.8 supervised Alexnet [A. Krizhevsky+, NIPS12] 89.0 Resnet110 [K. He+, CVPR16] 93.6 Accuracy
29. Copyright (C) 2019 DeNA Co.,Ltd. All Rights Reserved. Adversarial feature learning [J. Donahue+ ICLR17] n GeneratorEncoderadversarial training     G: z (uniform distribution) 
    G(z)   E:
    x E(x)  29 BiGAN
30. Copyright (C) 2019 DeNA Co.,Ltd. All Rights Reserved. Adversarial feature learning [J. Donahue+ ICLR17] n GeneratorEncoderadversarial training     G: z (uniform distribution) 
    G(z)   E:
    x E(x)  30 generated data e.g.) uniform distribution BiGAN
31. Copyright (C) 2019 DeNA Co.,Ltd. All Rights Reserved. Adversarial feature learning [J. Donahue+ ICLR17] n GeneratorEncoderadversarial training     G: z (uniform distribution) 
    G(z)   E:
    x E(x)  31 real data generated data e.g.) uniform distribution BiGAN
32. Copyright (C) 2019 DeNA Co.,Ltd. All Rights Reserved. Adversarial feature learning [J. Donahue+ ICLR17] n GeneratorEncoderadversarial training     G: z (uniform distribution) 
    G(z)   E:
    x E(x)  32 real data generated data e.g.) uniform distribution generated feature BiGAN
33. Copyright (C) 2019 DeNA Co.,Ltd. All Rights Reserved. Adversarial feature learning [J. Donahue+ ICLR17] n  33 
    GAN BiGAN xG(z) D
    real fake  • x: real data • G(z):  {x, E(x)}{G(z), z} D • x: real data • E(x): real data   • z: random noise • G(z) : 
34. Copyright (C) 2019 DeNA Co.,Ltd. All Rights Reserved. Adversarial feature learning [J. Donahue+ ICLR17] n
     34
35. Copyright (C) 2019 DeNA Co.,Ltd. All Rights Reserved. Adversarial feature learning [J. Donahue+ ICLR17] n  35
      
36. Copyright (C) 2019 DeNA Co.,Ltd. All Rights Reserved. Adversarial feature learning [J. Donahue+ ICLR17] n 
    
     : https://github.com/jeffdonahue/bigan   pytorch( ): https://github.com/9310gaurav/ali-pytorch 36
37. Copyright (C) 2019 DeNA Co.,Ltd. All Rights Reserved. VAEBiGAN
      37 n VAE > -:7<+0*  > #'(23 61 > Encoder, Decoder$! "%7<+#  > )89  n BiGAN > -:.0* > #'(23 61 > Encoder, Generator, Discriminator$! "%/61 > 5;=&,+GAN 4
38. Copyright (C) 2019 DeNA Co.,Ltd. All Rights Reserved. 
     n VAE;M<C N 2:/=  =*7 n GANBKD?5+ELF ↑AI0J∩deep learning∩.5'%(BKD?5>E LF n -13HEnd2End$#"64,9!&8G ) @   38
39. Copyright (C) 2019 DeNA Co.,Ltd. All Rights Reserved. FAQ n $#LGDO93;31Ca!S"'*)_W9 3;' b _W * IFXK_W93;E @`F_WUH? b IFYVC >B n Kaggle 0=8VAEVNU? J^) b \ #AENU? )'VAEV &(NU? )T+*) n VAE! A -  b !524</Z] >B31#,PQ!% b AE,VAE)&( R b 6.7:#! M[!% 39