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GENERATIVE ADVERSARIAL
NETWORKS
The most interesting idea in the last 10 years in ML.
- Yann LeCun, Facebook’s AI Research Director
BRIEF OVERVIEW
• Generative vs Discriminative models:
• Discriminative – Learn boundary between classes, P(y|x)
• Generative – Learn distribution of all classes, P(x,y)
• Advantages of generative model:
• Bayes rule for discrimination.
• Underlying structure of data.
• Two networks in GANs: Generator and Discriminator
• Evolution of GANs
• Applications
EVOLUTION OF GAN
• DCGAN
• Improved DCGAN
• Conditional GAN
• Info GAN
• StackGAN
• Others
THE WORKING
DISCRIMINATOR
• Our discriminator is a convolutional neural network that takes in an
image of size 28 x 28 x 1 as input and returns a single scalar number that
describes whether or not the input image is "real" or "fake"
GENERATOR
• You can think of the generator as a kind of reverse convolutional neural
network. A typical CNN like our discriminator network transforms a 2- or
3-dimensional matrix of pixel values into a single probability. A
generator, however, takes a d-dimensional vector of noise and
upsamples it to become a 28 x 28 image.
EXAMPLE SAMPLES
Generated samples after training a DCGAN
on celebA dataset with 202K images
Generated bedroom samples
ANIME FACE GENERATION USING
DCGAN
OTHER INTERESTING APPLICATION
• Caption to image generation.
• Results:
• the flower shown has yellow anther red pistil and bright red petals.
• this flower has petals that are yellow, white and purple and has dark lines
• the petals on this flower are white with a yellow center
• this flower has a lot of small round pink petals.
• this flower is orange in color, and has petals that are ruffled and rounded.
• the flower has yellow petals and the center of it is brown
• this flower has petals that are blue and white.
• Image generation from text
• High resolution caption to image generation.
• Uses two stacked GANS.
StackGAN

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Gan

  • 1. GENERATIVE ADVERSARIAL NETWORKS The most interesting idea in the last 10 years in ML. - Yann LeCun, Facebook’s AI Research Director
  • 2. BRIEF OVERVIEW • Generative vs Discriminative models: • Discriminative – Learn boundary between classes, P(y|x) • Generative – Learn distribution of all classes, P(x,y) • Advantages of generative model: • Bayes rule for discrimination. • Underlying structure of data. • Two networks in GANs: Generator and Discriminator • Evolution of GANs • Applications
  • 3. EVOLUTION OF GAN • DCGAN • Improved DCGAN • Conditional GAN • Info GAN • StackGAN • Others
  • 5. DISCRIMINATOR • Our discriminator is a convolutional neural network that takes in an image of size 28 x 28 x 1 as input and returns a single scalar number that describes whether or not the input image is "real" or "fake"
  • 6. GENERATOR • You can think of the generator as a kind of reverse convolutional neural network. A typical CNN like our discriminator network transforms a 2- or 3-dimensional matrix of pixel values into a single probability. A generator, however, takes a d-dimensional vector of noise and upsamples it to become a 28 x 28 image.
  • 7. EXAMPLE SAMPLES Generated samples after training a DCGAN on celebA dataset with 202K images Generated bedroom samples
  • 8. ANIME FACE GENERATION USING DCGAN
  • 9. OTHER INTERESTING APPLICATION • Caption to image generation. • Results: • the flower shown has yellow anther red pistil and bright red petals. • this flower has petals that are yellow, white and purple and has dark lines • the petals on this flower are white with a yellow center • this flower has a lot of small round pink petals. • this flower is orange in color, and has petals that are ruffled and rounded. • the flower has yellow petals and the center of it is brown • this flower has petals that are blue and white.
  • 10. • Image generation from text • High resolution caption to image generation. • Uses two stacked GANS. StackGAN