Metropolis-Hastings Generative Adversarial Networks Ryan Turner, - PowerPoint PPT Presentation

Jan 28, 2023 •205 likes •490 views

Metropolis-Hastings Generative Adversarial Networks Ryan Turner, Jane Hung, Eric Frank, Yunus Saatci, Jason Yosinski Poster #201 Typical GAN training Typical GAN training D tries to get here G tries to get here Typical GAN training D

Metropolis-Hastings Generative Adversarial Networks Ryan Turner, Jane Hung, Eric Frank, Yunus Saatci, Jason Yosinski Poster #201
Typical GAN training
Typical GAN training D tries to get here G tries to get here
Typical GAN training D tries to get here G tries to get here
Typical GAN training D tries to get here G tries to get here
Typical GAN training D tries to get here G tries to get here
Typical GAN training … gets stuck D tries to get here G tries to get here
MH-GAN helps you reach the star D tries to get here G tries to get here
MH-GAN helps you reach the star D tries to get here ● Wrap G and D to build better G' MH-GAN GAN G tries to get here Metropolis-Hastings Selector
MH recovers the true data distribution “Mixture of Gaussians” dataset [1] [1] Azadi et al. ICLR 2019.
MH recovers the true data distribution “Mixture of Gaussians” dataset [1] [1] Azadi et al. ICLR 2019.
MH recovers the true data distribution “Mixture of Gaussians” dataset [1] Dropped modes! [1] Azadi et al. ICLR 2019.
MH recovers the true data distribution “Mixture of Gaussians” dataset [1] [1] Azadi et al. ICLR 2019.
MH recovers the true data distribution “Mixture of Gaussians” dataset [1] [1] Azadi et al. ICLR 2019.
MH recovers the true data distribution “Mixture of Gaussians” dataset [1] Dropped modes! [1] Azadi et al. ICLR 2019.
MH recovers the true data distribution “Mixture of Gaussians” dataset [1] [1] Azadi et al. ICLR 2019.
Motivation for Metropolis-Hastings ● Use MCMC independence sampler : sample p D from G ● Given a perfect D and imperfect G, still obtain exact samples from true data distribution! ● Avoid densities in MCMC, just need density ratios : MH-GAN GAN Metropolis-Hastings Selector
Metropolis-Hastings as a post-processing step for generators MH-GAN GAN Metropolis-Hastings Selector
MH recovers the correct score distribution
MH recovers the correct score distribution Discriminator gives different scores to fakes
MH recovers the correct score distribution Score distribution now matches real data Discriminator gives different scores to fakes
Also… sample images
Progressive GAN (base)
Progressive GAN (base)
Progressive GAN PGAN + DRS (base) (calibrated)
Progressive GAN PGAN + DRS PGAN + MH-GAN (base) (calibrated) (calibrated)
Metropolis-Hastings GAN ne Hung, Eric Frank, Yunus Saatci, Jason Yosinski Ryan Turner, Jane Hung, Eric Frank, Yunus Saatci, Jason Yosinski Poster #201 https://github.com/uber-research/metropolis-hastings-gans
MH recovers the true data distribution 1) 1D mixture of 4 Gaussians, missing one mixture

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