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Introduction to Deep Generative Models
- 1. Introduction to Deep GenerativeModels Herman Dong Music and Audio Computing Lab (MACLab), Research Center for Information Technology Innovation, Academia Sinica
- 2. MuseGAN Learn about ourrecent work on using GAN to compose pop song at https://salu133445.github.io/musegan/ Hao-Wen Dong, Wen-Yi Hsiao, Li-Chia Yang and Yi-Hsuan Yang. 2017. MuseGAN: Symbolic-domain Music Generation and Accompaniment with Multi-track Sequential Generative Adversarial Networks. arXiv preprint arXiv:1709.06298.
- 3. Outline • Brief introductionto deep generative models • AE (Autoencoder) • VAE (Variational Autoencoder) • GAN (Generative Adversarial Networks) • AAE (Adversarial Autoencoder) • VAE/GAN • ADA (Adversarial Domain Adaption) • Reformulation • Graphical model representation • Connection to Wake-sleep Algorithm
- 4. AE (Autoencoder) • minimizereconstruction loss EX D E(X) D(E(X)) Lossreconstruction loss latent data
- 5. AE (Autoencoder) • minimizereconstruction loss reconstruction loss EX D E(X) D(E(X)) Loss latent data
- 6. AE (Autoencoder) • minimizereconstruction loss reconstruction loss EX D E(X) D(E(X)) Loss latent data
- 7. VAE (Variational Autoencoder) •minimize reconstruction loss • minimize distance between encoded latent distribution and prior distribution QX Q(X) PP(z) z ε latentdata
- 8. VAE (Variational Autoencoder) •minimize reconstruction loss • minimize distance between encoded latent distribution and prior distribution QX Q(X) p(z) Loss Loss PP(z) z ε reconstruction loss KL divergence latentdata ≈
- 9. VAE (Variational Autoencoder) •minimize reconstruction loss • minimize distance between encoded latent distribution and prior distribution QX Q(X) p(z) Loss Loss PP(z) z ε reconstruction loss KL divergence latentdata ≈
- 10. VAE (Variational Autoencoder) •minimize reconstruction loss • minimize distance between encoded latent distribution and prior distribution QX Q(X) p(z) Loss Loss PP(z) z ε reconstruction loss KL divergence latentdata μ σ ≈
- 11. VAE (Variational Autoencoder) •minimize reconstruction loss • minimize distance between encoded latent distribution and prior distribution QX Q(X) p(z) Loss Loss PP(z) z ε reconstruction loss KL divergence latentdata μ σ μ = z σ + ∙ ε ≈
- 12. VAE (Variational Autoencoder) •minimize reconstruction loss • minimize distance between encoded latent distribution and prior distribution QX Q(X) p(z) Loss Loss PP(z) z ε testing (generation) reconstruction loss KL divergence latentdata μ σ μ = z σ + ∙ ε ≈
- 13. GAN (Generative AdversarialNetwork) • minimize distance between the distribution of real data and generated samples Gz~p(z) G(z) D X 1/0 latent data
- 14. GAN (Generative AdversarialNetwork) • minimize distance between the distribution of real data and generated samples Gz~p(z) G(z) D X 1/0 adversarial training log(1-D(X)) + log(D(G(z))) log(1-D(G(z))) Make G(z) undistinguishable from real data for D Distinguish G(z) being fake from X being real latent data
- 15. GAN (Generative AdversarialNetwork) • minimize distance between the distribution of real data and generated samples Gz~p(z) G(z) D X 1/0 adversarial training log(1-D(X)) + log(D(G(z))) log(1-D(G(z))) Make G(z) undistinguishable from real data for D Distinguish G(z) being fake from X being real latent data
- 16. GAN (Generative AdversarialNetwork) • minimize distance between the distribution of real data and generated samples Gz~p(z) G(z) D X 1/0 adversarial training log(1-D(X)) + log(D(G(z))) log(1-D(G(z))) Make G(z) undistinguishable from real data for D Distinguish G(z) being fake from X being real latent data
- 17. GAN (Generative AdversarialNetwork) • minimize distance between the distribution of real data and generated samples Gz~p(z) G(z) D X 1/0 adversarial training log(1-D(X)) + log(D(G(z))) log(1-D(G(z))) Make G(z) undistinguishable from real data for D Distinguish G(z) being fake from X being real testing (generation) latent data
- 18. GAN vs VAE •GAN • Generator aim to fool the discriminator • Discriminator aim to distinguish generated data from real data • output images are sharper • higher diversity, lower stability • VAE • Objective: reconstruct real data • using pixel-to-pixel loss • output images are more blurred • lower diversity, higher stability GAN VAE A. B. L. Larsen, S. K. Sønderby, and O. Winther. Autoencoding beyond pixels using a learned similarity metric. arXiv preprint arXiv:1512.09300, 2015.
- 19. GAN vs VAE •GAN • Generator aim to fool the discriminator • Discriminator aim to distinguish generated data from real data • output images are sharper • higher diversity, lower stability • VAE • Objective: reconstruct real data • using pixel-to-pixel loss • output images are more blurred • lower diversity, higher stability GAN VAE A. B. L. Larsen, S. K. Sønderby, and O. Winther. Autoencoding beyond pixels using a learned similarity metric. arXiv preprint arXiv:1512.09300, 2015.
- 20. AAE (Adversarial Autoencoder) •minimize reconstruction loss • minimize distance between encoded latent distribution and prior distribution QX Q(X) p(z) Loss PP(z) z ε reconstruction loss latentdata ≈ Loss KL divergence
- 21. AAE (Adversarial Autoencoder) •minimize reconstruction loss • minimize distance between encoded latent distribution and prior distribution QX Q(X) p(z) Loss PP(z) reconstruction loss latentdata ≈ D 1/0 adversarial training Make Q(X) undistinguishable from real p(X) for D Distinguish Q(X) being fake from p(X) being real
- 22. AAE (Adversarial Autoencoder) •minimize reconstruction loss • minimize distance between encoded latent distribution and prior distribution QX Q(X) p(z) Loss PP(z) reconstruction loss latentdata ≈ D 1/0 adversarial training Make Q(X) undistinguishable from real p(X) for D Distinguish Q(X) being fake from p(X) being real
- 23. VAE/GAN Q Q(X) p(z) Loss PP(z) zε KL divergence latent data ≈ Loss reconstruction loss X VAE
- 24.
- 25. VAE/GAN Q Q(X) p(z) Loss PP(z) zε KL divergence latent data ≈ G(z) zG D1/0 X
- 26. VAE/GAN Q Q(X) p(z) Loss PP(z) zε KL divergence latent data ≈ G(z) z D1/0 (G) X
- 27. VAE/GAN Q Q(X) p(z) Loss PP(z) zε KL divergence latent data ≈ G(z) z D1/0 adversarial training Distinguish G(z) and P(z) being fake from X being real 𝓛𝓛GAN (G) X Make G(z) and P(z) undistinguishable from real data
- 28. VAE/GAN Q Q(X) p(z) Loss PP(z) zε KL divergence latent data ≈ G(z) z D1/0 adversarial training Distinguish G(z) and P(z) being fake from X being real 𝓛𝓛GAN Loss 𝓛𝓛DIS hidden representation reconstruction loss (G) X Make G(z) and P(z) undistinguishable from real data
- 29. VAE/GAN Q Q(X) p(z) Loss PP(z) zε KL divergence latent data ≈ G(z) z D1/0 adversarial training Distinguish G(z) and P(z) being fake from X being real 𝓛𝓛GAN Loss 𝓛𝓛DIS hidden representation reconstruction loss 𝓛𝓛𝐩𝐩𝐩𝐩𝐩𝐩𝐩𝐩𝐩𝐩 (G) X Make G(z) and P(z) undistinguishable from real data 𝓛𝓛 = 𝓛𝓛𝐩𝐩𝐩𝐩𝐩𝐩𝐩𝐩𝐩𝐩 + 𝓛𝓛DIS + 𝓛𝓛GAN
- 30. VAE/GAN Q Q(X) p(z) Loss PP(z) zε KL divergence latent data ≈ G(z) z D1/0 adversarial training Distinguish G(z) and P(z) being fake from X being real 𝓛𝓛GAN Loss 𝓛𝓛DIS hidden representation reconstruction loss 𝓛𝓛𝐩𝐩𝐩𝐩𝐩𝐩𝐩𝐩𝐩𝐩 (G) 𝓛𝓛 = 𝓛𝓛𝐩𝐩𝐩𝐩𝐩𝐩𝐩𝐩𝐩𝐩 + 𝓛𝓛DIS + 𝓛𝓛GAN X Make G(z) and P(z) undistinguishable from real data
- 31. VAE/GAN VAE VAEDIS GAN/VAE GAN Ground Truth VAE VAEDIS VAE/GAN Generation test Reconstructiontest A. B. L. Larsen, S. K. Sønderby, and O. Winther. Autoencoding beyond pixels using a learned similarity metric. arXiv preprint arXiv:1512.09300, 2015.
- 32. VAE/GAN Q Q(X) p(z) Loss PP(z) zε KL divergence latent data ≈ G(z) zG latent D1/0 adversarial training Distinguish G(z) and P(z) being fake from X being real 𝓛𝓛GAN Loss 𝓛𝓛DIS hidden representation reconstruction loss 𝓛𝓛𝐩𝐩𝐩𝐩𝐩𝐩𝐩𝐩𝐩𝐩 (G) 𝓛𝓛 = 𝓛𝓛𝐩𝐩𝐩𝐩𝐩𝐩𝐩𝐩𝐩𝐩 + 𝓛𝓛DIS + 𝓛𝓛GAN !? X
- 33. What’s going on? latentdata Loss reconstruction loss P XQ Q(X) (+ε) P(Q(X)) AE
- 34. What’s going on? latentdata Loss reconstruction loss Pz~p(z) Loss KL divergenceX Q Q(X) (+ε) P(Q(X)) VAE
- 35. What’s going on? latentdata Loss reconstruction loss Pz~p(z) 1/0D adversarial divergence X Q Q(X) (+ε) P(Q(X)) AAE
- 36. What’s going on? latentdata D1/0 adversarial lossP z~p(z)P(z) X GAN
- 37. What’s going on? latentdata D1/0 adversarial loss Loss hidden representation reconstruction loss Pz~p(z)P(z) Loss KL divergenceX Q Q(X) (+ε) P(Q(X)) VAE/GAN
- 38. What’s going on? latentdata Loss reconstruction loss D1/0 adversarial loss Loss hidden representation reconstruction loss Pz~p(z)P(z) Loss KL divergence 1/0D adversarial divergence X Q Q(X) (+ε) P(Q(X))
- 39. ADA (Adversarial DomainAdaption) • Goal: given labeled data in source domain, aim to classify unlabeled data in target domain. G G(Xsrc) data feature Xsrc C class
- 40. ADA (Adversarial DomainAdaption) • Goal: given labeled data in source domain, aim to classify unlabeled data in target domain. G G(Xsrc) Xtgt data feature Xsrc G(Xtgt) C class bad features
- 41. ADA (Adversarial DomainAdaption) • Goal: given labeled data in source domain, aim to classify unlabeled data in target domain. G G(Xsrc) Xtgt adversarial training Make G(Xtgt) and G(Xsrc) undistinguishable for D Distinguish G(Xtgt) as target domain from G(Xsrc) as source domain data feature Xsrc G(Xtgt) D 1/0 C class
- 42. ADA (Adversarial DomainAdaption) • Goal: given labeled data in source domain, aim to classify unlabeled data in target domain. G G(Xsrc) Xtgt adversarial training Make G(Xtgt) and G(Xsrc) undistinguishable for D Distinguish G(Xtgt) as target domain from G(Xsrc) as source domain data feature Xsrc G(Xtgt) D 1/0 C class
- 43. ADA (Adversarial DomainAdaption) • Goal: given labeled data in source domain, aim to classify unlabeled data in target domain. G G(Xsrc) Xtgt adversarial training Make G(Xtgt) and G(Xsrc) undistinguishable for D Distinguish G(Xtgt) as target domain from G(Xsrc) as source domain data feature Xsrc G(Xtgt) D 1/0 C class testing (classification)
- 44. On Unifying DeepGenerative Models z y x latent (data) data (feature) label
- 45. On Unifying DeepGenerative Models z y x latent (data) data (feature) label 𝒑𝒑 𝒛𝒛
- 46. On Unifying DeepGenerative Models • 𝑮𝑮𝜽𝜽 – 𝜃𝜃 are parameters in generator • 𝑫𝑫𝝓𝝓 – 𝜙𝜙 are parameters in generator z y x latent (data) data (feature) label 𝒑𝒑 𝒛𝒛 𝒙𝒙 = 𝑮𝑮𝜽𝜽 𝒛𝒛
- 47. On Unifying DeepGenerative Models • 𝑮𝑮𝜽𝜽 – 𝜃𝜃 are parameters in generator • 𝑫𝑫𝝓𝝓 – 𝜙𝜙 are parameters in generator • Solid line – generative process • Dashed line – inference process • Hollow arrow – deterministic transformation • Red arrow – adversarial mechanism • 𝒒𝒒𝝓𝝓 𝒓𝒓 𝒚𝒚 𝒙𝒙 denotes 𝒒𝒒𝝓𝝓 𝒚𝒚 𝒙𝒙 and 𝒒𝒒𝝓𝝓 𝟏𝟏 − 𝒚𝒚 𝒙𝒙 z y x 𝒒𝒒𝝓𝝓 𝒓𝒓 𝒚𝒚 𝒙𝒙 latent (data) data (feature) label 𝒑𝒑 𝒛𝒛 𝒙𝒙 = 𝑮𝑮𝜽𝜽 𝒛𝒛 𝒚𝒚 = � 𝟏𝟏, 𝑖𝑖𝑖𝑖 𝑥𝑥 𝑖𝑖𝑖𝑖 𝑖𝑖𝑖𝑖 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑 𝟎𝟎, 𝑖𝑖𝑖𝑖 𝑥𝑥 𝑖𝑖𝑖𝑖 𝑖𝑖𝑖𝑖 𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡 𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑 ADA 𝒚𝒚 = � 𝟏𝟏, 𝑖𝑖𝑖𝑖 𝑥𝑥 𝑖𝑖𝑖𝑖 𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟 𝟎𝟎, 𝑖𝑖𝑖𝑖 𝑥𝑥 𝑖𝑖𝑖𝑖 𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓 GAN
- 48. On Unifying DeepGenerative Models • 𝑮𝑮𝜽𝜽 – 𝜃𝜃 are parameters in generator • 𝑫𝑫𝝓𝝓 – 𝜙𝜙 are parameters in generator • Solid line – generative process • Dashed line – inference process • Hollow arrow – deterministic transformation • Red arrow – adversarial mechanism • 𝒒𝒒𝝓𝝓 𝒓𝒓 𝒚𝒚 𝒙𝒙 denotes 𝒒𝒒𝝓𝝓 𝒚𝒚 𝒙𝒙 and 𝒒𝒒𝝓𝝓 𝟏𝟏 − 𝒚𝒚 𝒙𝒙 z y x 𝒑𝒑𝜽𝜽 𝒙𝒙 𝒚𝒚 𝒒𝒒𝝓𝝓 𝒓𝒓 𝒚𝒚 𝒙𝒙 𝒑𝒑 𝑮𝑮𝜽𝜽 𝒛𝒛 𝒚𝒚 𝒚𝒚 = � 𝟏𝟏, 𝑖𝑖𝑖𝑖 𝑥𝑥 𝑖𝑖𝑖𝑖 𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟 𝟎𝟎, 𝑖𝑖𝑖𝑖 𝑥𝑥 𝑖𝑖𝑖𝑖 𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓 GAN 𝒚𝒚 = � 𝟏𝟏, 𝑖𝑖𝑖𝑖 𝑥𝑥 𝑖𝑖𝑖𝑖 𝑖𝑖𝑖𝑖 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑 𝟎𝟎, 𝑖𝑖𝑖𝑖 𝑥𝑥 𝑖𝑖𝑖𝑖 𝑖𝑖𝑖𝑖 𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡 𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑 ADA latent (data) data (feature) label 𝒑𝒑 𝒛𝒛 𝒙𝒙 = 𝑮𝑮𝜽𝜽 𝒛𝒛 GAN (ADA)
- 49. On Unifying DeepGenerative Models z y x latent data label VAE
- 50. On Unifying DeepGenerative Models z y x latent data label 𝒒𝒒𝜼𝜼 𝒛𝒛 𝒙𝒙, 𝒚𝒚 VAE
- 51. On Unifying DeepGenerative Models z y x 𝒒𝒒∗ 𝒓𝒓 𝒚𝒚 𝒙𝒙 latent data label 𝒒𝒒𝜼𝜼 𝒛𝒛 𝒙𝒙, 𝒚𝒚 = � 𝟏𝟏, 𝑖𝑖𝑖𝑖 𝑥𝑥 𝑖𝑖𝑖𝑖 𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟 𝟎𝟎, 𝑖𝑖𝑖𝑖 𝑥𝑥 𝑖𝑖𝑖𝑖 𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓 perfect discriminator VAE
- 52. On Unifying DeepGenerative Models z y x 𝒑𝒑𝜽𝜽 𝒙𝒙 𝒛𝒛, 𝒚𝒚 𝒒𝒒∗ 𝒓𝒓 𝒚𝒚 𝒙𝒙 latent data label 𝒒𝒒𝜼𝜼 𝒛𝒛 𝒙𝒙, 𝒚𝒚 = � 𝒑𝒑𝜽𝜽 𝒙𝒙 𝒛𝒛 , 𝑖𝑖𝑖𝑖 𝑦𝑦 = 0 𝒑𝒑𝒅𝒅𝒅𝒅𝒅𝒅𝒅𝒅 𝒙𝒙 , 𝑖𝑖𝑖𝑖 𝑦𝑦 = 1 = � 𝟏𝟏, 𝑖𝑖𝑖𝑖 𝑥𝑥 𝑖𝑖𝑖𝑖 𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟 𝟎𝟎, 𝑖𝑖𝑖𝑖 𝑥𝑥 𝑖𝑖𝑖𝑖 𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓 perfect discriminator VAE
- 53. degenerated adversarial discriminator On Unifying DeepGenerative Models z y x 𝒑𝒑𝜽𝜽 𝒙𝒙 𝒛𝒛, 𝒚𝒚 𝒒𝒒∗ 𝒓𝒓 𝒚𝒚 𝒙𝒙 latent data label 𝒒𝒒𝜼𝜼 𝒛𝒛 𝒙𝒙, 𝒚𝒚 = � 𝒑𝒑𝜽𝜽 𝒙𝒙 𝒛𝒛 , 𝑖𝑖𝑖𝑖 𝑦𝑦 = 0 𝒑𝒑𝒅𝒅𝒅𝒅𝒅𝒅𝒅𝒅 𝒙𝒙 , 𝑖𝑖𝑖𝑖 𝑦𝑦 = 1 = � 𝟏𝟏, 𝑖𝑖𝑖𝑖 𝑥𝑥 𝑖𝑖𝑖𝑖 𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟 𝟎𝟎, 𝑖𝑖𝑖𝑖 𝑥𝑥 𝑖𝑖𝑖𝑖 𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓 perfect discriminator VAE
- 54. degenerated adversarial discriminator adversary activated VAE (AAVAE) On UnifyingDeep Generative Models z y x 𝒑𝒑𝜽𝜽 𝒙𝒙 𝒛𝒛, 𝒚𝒚 𝒒𝒒∗ 𝒓𝒓 𝒚𝒚 𝒙𝒙 latent data label 𝒒𝒒𝜼𝜼 𝒛𝒛 𝒙𝒙, 𝒚𝒚 = � 𝒑𝒑𝜽𝜽 𝒙𝒙 𝒛𝒛 , 𝑖𝑖𝑖𝑖 𝑦𝑦 = 0 𝒑𝒑𝒅𝒅𝒅𝒅𝒅𝒅𝒅𝒅 𝒙𝒙 , 𝑖𝑖𝑖𝑖 𝑦𝑦 = 1 = � 𝟏𝟏, 𝑖𝑖𝑖𝑖 𝑥𝑥 𝑖𝑖𝑖𝑖 𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟 𝟎𝟎, 𝑖𝑖𝑖𝑖 𝑥𝑥 𝑖𝑖𝑖𝑖 𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓 perfect discriminator VAE
- 55. On Unifying DeepGenerative Models z y x 𝒑𝒑𝜽𝜽 𝒙𝒙 𝒚𝒚 𝒒𝒒𝝓𝝓 𝒓𝒓 𝒚𝒚 𝒙𝒙 latent data labelz y x 𝒑𝒑𝜽𝜽 𝒙𝒙 𝒚𝒚 𝒒𝒒𝝓𝝓 𝒓𝒓 𝒚𝒚 𝒙𝒙 𝒑𝒑 𝑮𝑮𝜽𝜽 𝒛𝒛 𝒚𝒚 latent data label 𝒑𝒑 𝒛𝒛 𝒙𝒙 = 𝑮𝑮𝜽𝜽 𝒛𝒛 GAN 𝒑𝒑 𝒛𝒛 𝒑𝒑 𝑮𝑮𝜽𝜽 𝒛𝒛 𝒚𝒚 𝒙𝒙 = 𝑮𝑮𝜽𝜽 𝒛𝒛 GAN
- 56. On Unifying DeepGenerative Models z y x 𝒑𝒑𝜽𝜽 𝒙𝒙 𝒚𝒚 𝒒𝒒𝝓𝝓 𝒓𝒓 𝒚𝒚 𝒙𝒙 latent data label 𝒒𝒒𝜼𝜼 𝒛𝒛 𝒙𝒙, 𝒚𝒚 InfoGAN z y x 𝒑𝒑𝜽𝜽 𝒙𝒙 𝒚𝒚 𝒒𝒒𝝓𝝓 𝒓𝒓 𝒚𝒚 𝒙𝒙 𝒑𝒑 𝑮𝑮𝜽𝜽 𝒛𝒛 𝒚𝒚 latent data label 𝒑𝒑 𝒛𝒛 𝒙𝒙 = 𝑮𝑮𝜽𝜽 𝒛𝒛 GAN degenerated code space 𝒙𝒙 = 𝑮𝑮𝜽𝜽 𝒛𝒛 𝒑𝒑 𝑮𝑮𝜽𝜽 𝒛𝒛 𝒚𝒚
- 57. On Unifying DeepGenerative Models z y x 𝒑𝒑𝜽𝜽 𝒙𝒙 𝒚𝒚 𝒒𝒒𝝓𝝓 𝒓𝒓 𝒚𝒚 𝒙𝒙 latent data labelz y x 𝒑𝒑𝜽𝜽 𝒙𝒙 𝒚𝒚 𝒒𝒒𝝓𝝓 𝒓𝒓 𝒚𝒚 𝒙𝒙 𝒑𝒑 𝑮𝑮𝜽𝜽 𝒛𝒛 𝒚𝒚 latent data label 𝒑𝒑 𝒛𝒛 𝒙𝒙 = 𝑮𝑮𝜽𝜽 𝒛𝒛 GAN 𝒑𝒑 𝒛𝒛 𝒑𝒑 𝑮𝑮𝜽𝜽 𝒛𝒛 𝒚𝒚 𝒙𝒙 = 𝑮𝑮𝜽𝜽 𝒛𝒛 GAN
- 58. On Unifying DeepGenerative Models x y z 𝒑𝒑𝜽𝜽 𝒛𝒛 𝒚𝒚 𝒒𝒒𝝓𝝓 𝒓𝒓 𝒚𝒚 𝒛𝒛 data latent labelz y x 𝒑𝒑𝜽𝜽 𝒙𝒙 𝒚𝒚 𝒒𝒒𝝓𝝓 𝒓𝒓 𝒚𝒚 𝒙𝒙 𝒑𝒑 𝑮𝑮𝜽𝜽 𝒛𝒛 𝒚𝒚 latent data label 𝒑𝒑 𝒛𝒛 𝒙𝒙 = 𝑮𝑮𝜽𝜽 𝒛𝒛 GAN 𝒑𝒑 𝒙𝒙 𝒛𝒛 = 𝑮𝑮𝜽𝜽 𝒙𝒙 𝒑𝒑 𝑮𝑮𝜽𝜽 𝒙𝒙 𝒚𝒚
- 59. On Unifying DeepGenerative Models x y z 𝒑𝒑𝜽𝜽 𝒛𝒛 𝒚𝒚 𝒒𝒒𝝓𝝓 𝒓𝒓 𝒚𝒚 𝒛𝒛 data latent label AAE z y x 𝒑𝒑𝜽𝜽 𝒙𝒙 𝒚𝒚 𝒒𝒒𝝓𝝓 𝒓𝒓 𝒚𝒚 𝒙𝒙 𝒑𝒑 𝑮𝑮𝜽𝜽 𝒛𝒛 𝒚𝒚 latent data label 𝒑𝒑 𝒛𝒛 𝒙𝒙 = 𝑮𝑮𝜽𝜽 𝒛𝒛 GAN 𝒑𝒑 𝒙𝒙 𝒛𝒛 = 𝑮𝑮𝜽𝜽 𝒙𝒙 𝒑𝒑 𝑮𝑮𝜽𝜽 𝒙𝒙 𝒚𝒚
- 60. GAN vs VAE zy x 𝒑𝒑𝜽𝜽 𝒙𝒙 𝒚𝒚 𝒒𝒒𝝓𝝓 𝒓𝒓 𝒚𝒚 𝒙𝒙 𝒑𝒑 𝑮𝑮𝜽𝜽 𝒛𝒛 𝒚𝒚 latent data label 𝒑𝒑 𝒛𝒛 𝒙𝒙 = 𝑮𝑮𝜽𝜽 𝒛𝒛 GAN z y x 𝒑𝒑𝜽𝜽 𝒙𝒙 𝒛𝒛, 𝒚𝒚 𝒒𝒒∗ 𝒓𝒓 𝒚𝒚 𝒙𝒙 latent data 𝒒𝒒𝜼𝜼 𝒛𝒛 𝒙𝒙, 𝒚𝒚 VAE label
- 61. InfoGAN vs VAE zy x 𝒑𝒑𝜽𝜽 𝒙𝒙 𝒛𝒛, 𝒚𝒚 𝒒𝒒∗ 𝒓𝒓 𝒚𝒚 𝒙𝒙 latent data 𝒒𝒒𝜼𝜼 𝒛𝒛 𝒙𝒙, 𝒚𝒚 VAE labelz y x 𝒑𝒑𝜽𝜽 𝒙𝒙 𝒚𝒚 𝒒𝒒𝝓𝝓 𝒓𝒓 𝒚𝒚 𝒙𝒙 latent data label 𝒒𝒒𝜼𝜼 𝒛𝒛 𝒙𝒙, 𝒚𝒚 InfoGAN 𝒑𝒑 𝑮𝑮𝜽𝜽 𝒛𝒛 𝒚𝒚 𝒙𝒙 = 𝑮𝑮𝜽𝜽 𝒛𝒛
- 62. InfoGAN vs AAE zy x 𝒑𝒑𝜽𝜽 𝒙𝒙 𝒚𝒚 𝒒𝒒𝝓𝝓 𝒓𝒓 𝒚𝒚 𝒙𝒙 latent data label 𝒒𝒒𝜼𝜼 𝒛𝒛 𝒙𝒙, 𝒚𝒚 InfoGAN x y z 𝒑𝒑𝜽𝜽 𝒛𝒛 𝒚𝒚 𝒒𝒒𝝓𝝓 𝒓𝒓 𝒚𝒚 𝒛𝒛 latent label 𝒛𝒛 = 𝑮𝑮𝜽𝜽 𝒙𝒙 𝒑𝒑 𝑮𝑮𝜽𝜽 𝒙𝒙 𝒚𝒚 𝒒𝒒𝜼𝜼 𝒙𝒙 𝒛𝒛, 𝒚𝒚 data 𝒑𝒑 𝑮𝑮𝜽𝜽 𝒛𝒛 𝒚𝒚 AAE 𝒙𝒙 = 𝑮𝑮𝜽𝜽 𝒛𝒛
- 63. Wake-sleep Algorithm • 𝒉𝒉- general latent variables • 𝝀𝝀 - general parameters • 𝜽𝜽 - generator parameters • In wake phase, update 𝜽𝜽 by fitting 𝒑𝒑𝜽𝜽 𝒙𝒙|𝒉𝒉 to 𝒙𝒙 and 𝒉𝒉 inferred by 𝒒𝒒𝝀𝝀 𝒉𝒉|𝒙𝒙 . • In sleep phase, update 𝝀𝝀 based on generated samples. • VAE: 𝒉𝒉 → 𝒛𝒛, 𝝀𝝀 → 𝜼𝜼 • GAN: 𝒉𝒉 → 𝒚𝒚, 𝝀𝝀 → 𝝓𝝓 Wake: max 𝜽𝜽 𝔼𝔼𝒒𝒒𝝀𝝀 𝒉𝒉|𝒙𝒙 𝒑𝒑𝒅𝒅𝒅𝒅𝒅𝒅𝒅𝒅 𝒙𝒙 log 𝑝𝑝𝜃𝜃 𝑥𝑥 ℎ Sleep: max 𝜆𝜆 𝔼𝔼𝒑𝒑𝜽𝜽 𝒙𝒙|𝒉𝒉 𝒑𝒑 𝒉𝒉 log 𝑞𝑞𝜆𝜆 ℎ 𝑥𝑥
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