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マルチモーダルデータにおける少数ペアデータからの学習

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京都大学x理研AIPxエクサウィザーズ 機械学習勉強会1で発表頂いた山田誠准教授の資料です

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マルチモーダルデータにおける少数ペアデータからの学習

  1. 1. 1 AIP Joint work with Masashi Sugiyama, Michalis Raptis, Leonid Sigal, Tanmoy Mukherjee, Timothy Hospedales
  2. 2. n n 2003 n 2005 Colorado State University n 2005-2007 n 2007-2010 n 2010 n 2010-2012 ( ) n 2012 Carnegie Mellon University, Disney Research, Visitor n 2012-2013 NTT n 2013-2015 Yahoo Labs (Research Scientist) n 2015-2017 n 2017- AIP n 2017- Aalto University, ( ) n 2018- n n ( ) n ( , , Generative Adversarial Nets ) n (NTT CS , Yahoo Labs, , ) n 2
  3. 3. n n n n3 n n n Yamada et al. (NIPS 2011) Liu, Yamada, Collier, & Sugiyama(NN 2012) Yamada, Sigal, & Raptis (TPAMI 2014) Yamada, Sugiyama, & Matsui (SP 2010) Yamada & Sugiyama (AAAI 2011) Yamada et al. (NIPS 2011) 3
  4. 4. n n n n (Visualization) Yamada et al. (AISTATS 2011, TPAMI 2015, ECCV 2018 (Oral)) Yamada, Niu, Takagi & Sugiyama (ACML 2011) Yamada, Sugiyama, & Sese (AAAI 2010, Machine Learning 2014) Sugiyama, Yamada, Kimura & Hachiya (ICML 2011) 4
  5. 5. n n n n n n n Yamada et al. (NECO 2014) Yamada et al. (TKDE 2018) Gunasekar, et al. (AISTATS 2015) Yamada et al. (KDD 2017) Yamada et al. (IJCAI 2013) Gao, Yamada, Kaski, Mamitsuka, & Zhu. (IJCAI 2016) Chang, Yamada, Ortega, & Liu. (ICDM 2014) Chang, Tang, Yin, Yamada, & Liu. (IJCAI 2016) U= ⇥ V > V 0> U0 A1 A2 A3 5 Yamada et al. (AISTATS 2017)
  6. 6. Deep Learning (CNN, RNN) (GBDT) Lasso ( , ) (SNPs, , etc.) Matrix/Tensor Factorization () 10^3 10^4 10^5 10^6 10^7 10^8 10^2 10^4 10^6 10^8 ! 6 ?
  7. 7. Deep Learning (CNN, RNN) (GBDT) Lasso ( , ) (SNPs, , etc.) Matrix/Tensor Factorization () 10^3 10^4 10^5 10^6 10^7 10^8 10^2 10^4 10^6 10^8 ! 7 ?
  8. 8. (CDOM) Jebara ALT 2004, Quadrianto 2009, Yamada 2011 n : … Image domain Frame domain … Haghighi (ACL 2008), Lample et al. https://arxiv.org/abs/1804.07755 n ncycleGAN Japanese Kanji (Mountain) 8
  9. 9. CDOM with SMI n 1 : n 2 : n : n : n : n SMI . 9 Yamada & Sugiyama(AISTATS 2011)
  10. 10. n : e.g., 10
  11. 11. n , , 2006 . n 2008 2012 . n 2016 nGenerative Adversarial Networks (GAN) nApproximate Bayesian Computation (ABC) nMutual Information estimation nDeepMind nhttp://blog.shakirm.com/2018/01/machine-learning-trick-of- the-day-7-density-ratio-trick/ 11
  12. 12. n : SMI n : fit . nLeast-Squares Mutual Information (LSMI): Least-Squares Mutual Information (LSMI)12 Suzuki, Sugiyama, Kanamori, & Sese (BMC Bioinformatics 2009) :
  13. 13. Toy Experiments: LSMI 13 : 2.001 :-0.0004
  14. 14. nImage data nFlickr (color) nFrame data nRectangular, “Mountain” nColor image: Lab color space nGray scale images: gray scale value 14
  15. 15. Data Visualization: Result nFlickr (rectangular/”mountain” frames) Similar color images are located closely! 15
  16. 16. Deep Matching Autoencoders n n 16 DMAE Text Modality Image Modali ty min ⇥x,⇥y,⇧ nX i=1 kxi fx(gx(xi))k2 2 + kyi fy(gy(yi)))k2 2 D⇧({gx(xi), gy(y⇡(i))}n i=1)
  17. 17. 17 Image … … … … Text gx(x) fx(gx(x)) … … … … fy(gy(y))gy(y) {xi}n i=1 {yi}n i=1 {(x0 j, y0 j)}n0 j=1 Paired data Unpaired Data D⇧({gx(xi), gy(y⇡(i))}n i=1) Deep Matching Autoencoders
  18. 18. Take home message n (CDOM) n n n nDeep Matching Autoencoder n CDOM n n 18

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