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DiscoGAN - Learning to Discover Cross-Domain Relations with Generative Adversarial Networks

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Taeksoo Kim
Taeksoo Kim

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Learning to Discover Cross-Domain Relations with Generative Adversarial Networks Taeksoo Kim, Moonsu Cha, Hyunsoo Kim, Jung Kwon Lee and Jiwon Kim DiscoGAN 1 SKT-Brain
SKT-Brain Motivation Discovering Cross-Domain Relations Collecting paired dataset is difficult Hair color Gender(Taking off) Eyeglasses Bag to Shoe 2
SKT-Brain Motivation Can we translate images WITHOUT paired datasets? 3 Discovering Cross-Domain Relations
SKT-Brain Motivation Humans can naturally DISCOVER cross-domain relations 4 Discovering Cross-Domain Relations
SKT-Brain Motivation Natural translation is changing hair color only 5 Discovering Cross-Domain Relations Portraits with blond hair Portraits with black hair
SKT-Brain Generative Adversarial Networks (GANs) Related Works I. Goodfellow et al., “Generative Adversarial Networks”, 2014 Pathak et al., “Context Encoders: Feature Learning by Inpainting”, CVPR 2016 C. Ledig et al., “Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network”, 2016 6 Image Completion Super Resolution
SKT-Brain Translation Model Train a translation function GAB GAB GAB A B 7
SKT-Brain Vanilla GAN Model 8 𝐺%& ∶ ℝ% *×,×- → ℝ& *×,×- 𝐷& ∶ ℝ% *×,×- → [0, 1] 𝑫 𝑩 𝑥%& 𝑥& 𝐿9: 𝑮 𝑨𝑩 𝑥%
SKT-Brain Vanilla GAN Model 9 𝐺%& ∶ ℝ% *×,×- → ℝ& *×,×- 𝐷& ∶ ℝ% *×,×- → [0, 1] 𝑥%& = 𝐺%&(𝑥%) 𝑫 𝑩 𝑥%& 𝑥& 𝐿9: 𝑮 𝑨𝑩 𝑥%
SKT-Brain Vanilla GAN Model Generator Loss Discriminator Loss 10 𝐿@%A: = −𝔼DE~GE [𝑙𝑜𝑔𝐷&(𝐺%&(𝑥%))] 𝐿9: = −𝔼D:~G: 𝑙𝑜𝑔𝐷& 𝑥% −𝔼DE~GE log (1 − 𝐷& 𝐺%&(𝑥% ) 𝑫 𝑩 𝑥%& 𝑥& 𝐿9: 𝑮 𝑨𝑩 𝑥%
SKT-Brain Vanilla GAN Model A B Mappings may not be meaningful 11 GAB 𝑫 𝑩 𝑥%& 𝑥& 𝐿9: 𝑮 𝑨𝑩 𝑥%
SKT-Brain Inverse-translation Model An inverse-translation function GBA 12 GAB GAB A B GBA GBA
SKT-Brain Model GAN with reconstruction 13 𝐺&% ∶ ℝ& *×,×- → ℝ% *×,×- 𝑥%&% = 𝐺&% 𝑥%& = 𝐺&% ∘ 𝐺%&(𝑥%)𝑥%&% 𝑫 𝑩 𝑮 𝑩𝑨 𝑥%& 𝑥& 𝐿OPAQRE 𝐿9: 𝑮 𝑨𝑩 𝑥%
SKT-Brain Model 14 GAN with reconstruction 𝐺&% ∶ ℝ& *×,×- → ℝ% *×,×- 𝑥%&% = 𝐺&% 𝑥%& = 𝐺&% ∘ 𝐺%&(𝑥%) 𝐿OPAQRE = 𝑑(𝑥%&%, 𝑥%) 𝑥%&% 𝑮 𝑩𝑨 𝑥%& 𝑥& 𝐿OPAQRE 𝐿9: 𝑮 𝑨𝑩 𝑥% 𝑫 𝑩
SKT-Brain Model 15 GAN with reconstruction 𝐿@E: = 𝐿@%A& + 𝐿OPAQR% 𝐿9: = −𝔼D:~G: 𝑙𝑜𝑔𝐷& 𝑥% −𝔼DE~GE log (1 − 𝐷& 𝐺%&(𝑥% )) Generator Loss Discriminator Loss 𝑥%&% 𝑮 𝑩𝑨 𝑥%& 𝑥& 𝐿OPAQRE 𝐿9: 𝑮 𝑨𝑩 𝑥% 𝑫 𝑩
SKT-Brain Model 16 GAN with reconstruction 𝐿@E: = 𝐿@%A& + 𝐿OPAQR% 𝐿9: = −𝔼D:~G: 𝑙𝑜𝑔𝐷& 𝑥% −𝔼DE~GE log (1 − 𝐷& 𝐺%&(𝑥% )) Generator Loss Discriminator Loss 𝑥%&% 𝑮 𝑩𝑨 𝑥%& 𝑥& 𝐿OPAQRE 𝐿9: 𝑮 𝑨𝑩 𝑥% 𝑫 𝑩
SKT-Brain Model 17 GAN with reconstruction 𝐿@E: = 𝐿@%A& + 𝐿OPAQR% 𝐿9: = −𝔼D:~G: 𝑙𝑜𝑔𝐷& 𝑥% −𝔼DE~GE log (1 − 𝐷& 𝐺%&(𝑥% )) Generator Loss Discriminator Loss 𝑥%&% 𝑮 𝑩𝑨 𝑥%& 𝑥& 𝐿OPAQRE 𝐿9: 𝑮 𝑨𝑩 𝑥% 𝑫 𝑩
SKT-Brain Model 18 GAN with reconstruction 𝐿@E: = 𝐿@%A& + 𝐿OPAQR% 𝐿9: = −𝔼D:~G: 𝑙𝑜𝑔𝐷& 𝑥% −𝔼DE~GE log (1 − 𝐷& 𝐺%&(𝑥% )) Generator Loss Discriminator Loss 𝑥%&% 𝑮 𝑩𝑨 𝑥%& 𝑥& 𝐿OPAQRE 𝐿9: 𝑮 𝑨𝑩 𝑥% 𝑫 𝑩
SKT-Brain Model 19 Generator Loss Discriminator Loss GAN with reconstruction 𝐿@E: = 𝐿@%A& + 𝐿OPAQR% 𝐿9: = −𝔼D:~G: 𝑙𝑜𝑔𝐷& 𝑥% −𝔼DE~GE log (1 − 𝐷& 𝐺%&(𝑥% )) 𝑥%&% 𝑮 𝑩𝑨 𝑥%& 𝑥& 𝐿OPAQRE 𝐿9: 𝑮 𝑨𝑩 𝑥% 𝑫 𝑩
SKT-Brain Model 20 GAB A B GBA GAB GAN with reconstruction 𝑥%&% 𝑮 𝑩𝑨 𝑥%& 𝑥& 𝐿OPAQRE 𝐿9: 𝑮 𝑨𝑩 𝑥% 𝑫 𝑩
SKT-Brain Model 21 GAB A B GAB GBA GBA GAN with reconstruction 𝑥%&% 𝑮 𝑩𝑨 𝑥%& 𝑥& 𝐿OPAQRE 𝐿9: 𝑮 𝑨𝑩 𝑥% 𝑫 𝑩
SKT-Brain Model 22 GAB A B Add a model that performs the mirror task DiscoGAN
SKT-Brain Model 23 GAB A B GBA DiscoGAN Add a model that performs the mirror task
SKT-Brain Model 24 A B GBA GAB GBA DiscoGAN Add a model that performs the mirror task
SKT-Brain Model 25 A B GAB GBA GAB GBA DiscoGAN Add a model that performs the mirror task
SKT-Brain Model DiscoGAN GAB A B GBA 26 𝑥%&% 𝑫 𝑩 𝑮 𝑩𝑨𝑥%& 𝑥& 𝐿OPAQRE 𝐿9: 𝑮 𝑨𝑩 𝑥%
SKT-Brain Model DiscoGAN 27 GAB A B GBA 𝐿OPAQR: 𝑥&% 𝐿9E 𝑥&%&𝑥& 𝑫 𝑨 𝑮 𝑨𝑩𝑮 𝑩𝑨 𝑥%
SKT-Brain Model DiscoGAN Total Generator Loss Total Discriminator Loss 28 𝐿@ = 𝐿@%& + 𝐿@E: 𝐿9 = 𝐿9% + 𝐿9& 𝐿OPAQR: 𝑥%&% 𝑥&% 𝐿9E 𝑫 𝑩 𝑮 𝑩𝑨𝑥%& 𝑥&%&𝑥& 𝑫 𝑨 𝐿OPAQRE 𝐿9: 𝑮 𝑨𝑩 𝑮 𝑨𝑩 𝑮 𝑩𝑨 𝑥%
SKT-Brain Model DiscoGAN Total Generator Loss Total Discriminator Loss 29 𝐿@ = 𝐿@%& + 𝐿@E: 𝐿9 = 𝐿9% + 𝐿9& 𝐿OPAQR: 𝑥%&% 𝑥&% 𝐿9E 𝑫 𝑩 𝑮 𝑩𝑨𝑥%& 𝑥&%&𝑥& 𝑫 𝑨 𝐿OPAQRE 𝐿9: 𝑮 𝑨𝑩 𝑮 𝑨𝑩 𝑮 𝑩𝑨 𝑥%
SKT-Brain Model DiscoGAN Total Generator Loss Total Discriminator Loss 30 𝐿@ = 𝐿@%& + 𝐿@E: 𝐿9 = 𝐿9% + 𝐿9& 𝐿OPAQR: 𝑥%&% 𝑥&% 𝐿9E 𝑫 𝑩 𝑮 𝑩𝑨𝑥%& 𝑥&%&𝑥& 𝑫 𝑨 𝐿OPAQRE 𝐿9: 𝑮 𝑨𝑩 𝑮 𝑨𝑩 𝑮 𝑩𝑨 𝑥%
SKT-Brain Experimental Results Car → Car (Rotation) 31 Source, target domain : Rotating 3D cars with 15° interval
SKT-Brain Experimental Results Car → Car (Rotation) 32 Source, target domain : Rotating 3D cars with 15° interval Input
SKT-Brain Experimental Results Car → Car (Rotation) 33 Source, target domain : Rotating 3D cars with 15° interval Baseline 1 Input
SKT-Brain Experimental Results Car → Car (Rotation) 34 Source, target domain : Rotating 3D cars with 15° interval Baseline 1 Input
SKT-Brain Experimental Results Car → Car (Rotation) 35 Source, target domain : Rotating 3D cars with 15° interval Baseline 1 Input
SKT-Brain Experimental Results Car → Car (Rotation) 36 Source, target domain : Rotating 3D cars with 15° interval Baseline 1 Input
SKT-Brain Experimental Results Car → Car (Rotation) 37 Source, target domain : Rotating 3D cars with 15° interval Baseline 1 Input Baseline 2
SKT-Brain Experimental Results Car → Car (Rotation) 38 Source, target domain : Rotating 3D cars with 15° interval Baseline 1 Input Baseline 2
SKT-Brain Experimental Results Car → Car (Rotation) 39 Source, target domain : Rotating 3D cars with 15° interval Baseline 1 Input Baseline 2
SKT-Brain Experimental Results Car → Car (Rotation) 40 Source, target domain : Rotating 3D cars with 15° interval Baseline 1 Input Baseline 2
SKT-Brain Experimental Results Car → Car (Rotation) 41 Baseline 1 Source, target domain : Rotating 3D cars with 15° interval Input Baseline 2 DiscoGAN
SKT-Brain Experimental Results Car → Car (Rotation) 42 Baseline 1 Source, target domain : Rotating 3D cars with 15° interval Input Baseline 2 DiscoGAN
SKT-Brain 43 Male → Female Experimental Results - DiscoGAN Source domain : Male face images Target domain : Female face images
SKT-Brain Male → Female Experimental Results - DiscoGAN 44 Source domain : Male face images Target domain : Female face images Input
SKT-Brain Male → Female Experimental Results - DiscoGAN 45 Input Mapping Result Source domain : Male face images Target domain : Female face images
SKT-Brain Experimental Results - DiscoGAN Car → Face 46 Source domain : Rotating 3D cars Target domain : Rotating 3D faces
SKT-Brain Experimental Results - DiscoGAN Car → Face 47 Source domain : Rotating 3D cars Target domain : Rotating 3D faces Input
SKT-Brain Experimental Results - DiscoGAN Car → Face 48 Source domain : Rotating 3D cars Target domain : Rotating 3D faces Input Mapping Result
SKT-Brain Experimental Results - DiscoGAN 49 Handbag → Shoe Source domain : Handbag images Target domain : Shoe images
SKT-Brain Experimental Results - DiscoGAN 50 Handbag → Shoe Source domain : Handbag images Target domain : Shoe images Input
SKT-Brain Experimental Results - DiscoGAN 51 Handbag → Shoe Source domain : Handbag images Target domain : Shoe images Input Mapping Result
SKT-Brain Conclusion 52 Summary • We presents a learning method to discover cross-domain relations with a generative adversarial network. • Our approach works without any explicit pair labels and learns to relate datasets from very different domains. Whenever you get bored, just download any two datasets and put them into DiscoGAN. It will give you an interesting result that you didn’t think of! Code is available on GitHub (https://github.com/SKTBrain/DiscoGAN). Follow-up work is available on arXiv (T. Kim at el, “Unsupervised Visual Attribute Transfer with Reconfigurable Generative Adversarial Networks”)
SKT-Brain Future Work (on arXiv) 53 Future Work Input Hair color [black / brown / blond] Bang hair [with / without] Smile [with / without] T. Kim et al., “Unsupervised Visual Attribute Transfer with Reconfigurable Generative Adversarial Networks”, 2017 Extension to multiple domains (single network!)
SKT-Brain Future Work (on arXiv) 54 Future Work Extension to attribute transfer Bang hair transfer Smile transfer T. Kim et al., “Unsupervised Visual Attribute Transfer with Reconfigurable Generative Adversarial Networks”, 2017
SKT-Brain Thank you! 55

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