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Generative adversarial text to image synthesis

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Universitat Politècnica de Catalunya
Universitat Politècnica de Catalunya

Slides by Víctor Garcia about the paper: Reed, Scott, Zeynep Akata, Xinchen Yan, Lajanugen Logeswaran, Bernt Schiele, and Honglak Lee. "Generative adversarial text to image synthesis." ICML 2016.

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Generative Adversarial Text to Image Synthesis Scott Reed, Zeynep Akata, Xinchen Yan, Lajanugen Logeswaran [GitHub] [Arxiv] Slides by Víctor Garcia [GDoc] Computer Vision Reading Group (30/09/2016)
Index ● Introduction ● State of the Art ● Method ○ Network Architecture ○ Losses ● Experiments ○ Qualitative Results ○ Sentence interpolation ○ Style Transfer ● Conclusions
Introduction Text → Image GANs
Index ● Introduction ● State of the Art ● Method ○ Network Architecture ○ Losses ● Experiments ○ Qualitative Results ○ Sentence interpolation ○ Style Transfer ● Conclusions
GANs Discriminator 1/0 True World Fake Generator
GANs Discriminator D(·) 1/0 True World Fake Generator q(x) x G(z) zx’
GANs Discriminator D(·) MAX → E[log(D(X))] True World Fake Generator q(x) x G(z) zx’
GANs Discriminator D(·) MAX → E[log(D(X))] + E[ log(1 - D(G(Z))) ] True World Fake Generator q(x) x G(z) zx’
GANs Discriminator D(·) MAX → E[log(D(X))] + E[ log(1 - D(G(Z))) ] True World Fake Generator q(x) x G(z) zx’
GANs Discriminator D(·) True World Fake Generator q(x) x G(z) zx’ MIN → E[ log(1 - D(G(Z))) ]
GANs with Join Distributions How do we generate the image from text?
GANs with Join Distributions How do we generate the image from text? Discriminator 1/0 f(x,t) f(x’,t)
GANs with Join Distributions Discriminator 1/0 Real Image + Text Gen. Image + Text Generator + Text
GANs with Join Distributions Discriminator 1/0 Real Image + Text Gen. Image + Text Generator + Text
Text Embeddding In order to represent the text in a vector... MIN WHERE
Text Embeddding In order to represent the text in a vector... MIN WHERE This is the recurrent text encoder
Index ● Introduction ● State of the Art ● Method ○ Network Architecture ○ Losses ● Experiments ○ Qualitative Results ○ Sentence interpolation ○ Style Transfer ● Conclusions
Network Architecture
Losses - CLS log(D(x,t)) log(1-D(G(z,t))) True Image + True Text Fake Image + True Text Real Images match the text content?
Losses - CLS log(D(x,t)) log(1-D(G(z,t))) log(1-D(G(zi,tk))) True Image + True Text Fake Image + True Text True Image (i) + True Text (j) Unmatched
Losses - INT They train interpolating between different text embedding vector (t1~t2). So the generator learns to fill GAPS on the data manifold.
Index ● Introduction ● State of the Art ● Method ○ Network Architecture ○ Losses ● Experiments ○ Qualitative Results ○ Sentence interpolation ○ Style Transfer ● Conclusions
Qualitative Results - Birds
Sentence Interpolation Gen. z0 + Text1 Gen. z1 + Text3 Gen. z0 + Text2 Gen. z1 + Text4
Disentangling style and content Generator. z + Text If ‘text’ is describing the content? What is ‘z’ describing?
Disentangling style and content Generator. z + Text If ‘text’ is describing the content? What is ‘z’ describing? Style → Pose, Background…, let’s extract ‘z’
Disentangling style and content z0 z1 z2 z3 z4 z5
Qualitative Results - Flowers
Qualitative Results - MSCOCO
Conclusions Discriminator 1/0 f(x,t) f(x’,t) x~t
Generative adversarial text to image synthesis

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