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Introduction to ambient GAN

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JaeJun Yoo
JaeJun Yoo

This paper proposes AmbientGAN, which trains a generative adversarial network using partial or noisy observations rather than fully observed samples. AmbientGAN trains the discriminator on the measurement domain rather than the raw data domain, allowing the generator to be trained without needing large amounts of good training data. The paper proves it is theoretically possible to recover the original data distribution even when the measurement process is not invertible. It presents experimental results showing AmbientGAN can generate high quality samples and recover the underlying data distribution from various types of lossy and noisy measurements.

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AmbientGAN: Generative Models From Lossy Measurements ICLR 2018 Reading Group Jaejun Yoo Clova ML / NAVER 12th Mar, 2018 by A. Bora, E. Price, A.G. Dimakis
Motivation “How can we train our generative models with partial, noisy observations” In many settings, it is expensive or even impossible to obtain fully-observed samples, but economical to obtain partial, noisy samples. Why do we care?
This paper proposes • AmbientGAN: train the discriminator not on the raw data domain but on the measurement domain. • Propose the way to train the generative model with a noisy, corrupted, or missing data. • Prove that it is theoretically possible to recover the original true data distribution even though the measurement process is not invertible.
This paper proposes (preview)
This paper proposes (preview)
Model Architecture (preview)
Generative Adversarial Networsks Diagram of Standard GAN Gaussian noise as an input for G z G D x Real or Fake? 지폐위조범 경찰
Limitation of GAN • Training Stability • Forgetting problem • Requires Good (or fully observed) Training Samples!! • To train the generator, we need a lot of good images • In many settings, it is expensive or even impossible to obtain fully-observed samples, but economical to obtain partial, noisy samples.
Limitation of GAN • Training Stability • Forgetting problem • Requires Good (or fully observed) Training Samples!! • To train the generator, we need a lot of good images • In many settings, it is expensive or even impossible to obtain fully-observed samples, but economical to obtain partial, noisy samples. Compressed sensing attempts to address this problem by exploiting the models of the data structure; sparsity. Bora et al. ICML 2017, “Compressed Sensing using Generative Models”
https://www.cs.utexas.edu/~ashishb/assets/csgm/slides_google_ashish.pdf Bora et al. 2017, “Compressed Sensing using Generative Models” Compressed Sensing Paper in “a” Slide
Chicken and Egg problem • Okay, they showed it is possible to solve the problem with a small number of (good) measurements by using Generative Models! • But, what if it is even not possible to gather the good data in the first place? • How can we collect enough data to train a generative model to start with?
Model Architecture
Measurements? (in compressed sensing)
Measurements? (in signal processing)
Measurements? (in this paper) • Block-Pixels: with 𝑝, make image pixel 0 • Conv + Noise: Gaussian kernel blur + add random Gaussian noise • Block-Patch: make 𝑘 × 𝑘 patch 0 • Keep-Patch: make 0 except 𝑘 × 𝑘 patch • Extract-Patch: use 𝑘 × 𝑘 patch as input • Pad-Rotate-Project: zero padding + rotate + vertical 1D projection • Pad-Rotate-Project-𝛉: Pad-Rotate-Project and include θ as an additional information for the measurement • Gaussian-Projection: Sample a random Gaussian vector and project
Dataset and Model Architectures
Baselines
Experimental Results
Experimental Results
Experimental Results
Experimental Results
Experimental Results
Experimental Results
Theory
In Conclusion • Using AmbientGAN, it is possible to train the generator without the fully-observed data • In theory, it is possible to find the true distribution by training the generator when the measurement process is invertible and differentiable • Empirically, it is possible to recover the good data distribution even though the measurement process is not clearly known.
Possible Applications? • OCR • Gayoung’s Webtoon data • Adding Reconstruction loss and Cyclic loss • Learnable 𝒇 𝜽 −𝟏 ⋅ by FC • etc.

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Introduction to ambient GAN

  • 1. AmbientGAN: Generative Models From Lossy Measurements ICLR 2018 Reading Group Jaejun Yoo Clova ML / NAVER 12th Mar, 2018 by A. Bora, E. Price, A.G. Dimakis
  • 2. Motivation “How can we train our generative models with partial, noisy observations” In many settings, it is expensive or even impossible to obtain fully-observed samples, but economical to obtain partial, noisy samples. Why do we care?
  • 3. This paper proposes • AmbientGAN: train the discriminator not on the raw data domain but on the measurement domain. • Propose the way to train the generative model with a noisy, corrupted, or missing data. • Prove that it is theoretically possible to recover the original true data distribution even though the measurement process is not invertible.
  • 7. Generative Adversarial Networsks Diagram of Standard GAN Gaussian noise as an input for G z G D x Real or Fake? 지폐위조범 경찰
  • 8. Limitation of GAN • Training Stability • Forgetting problem • Requires Good (or fully observed) Training Samples!! • To train the generator, we need a lot of good images • In many settings, it is expensive or even impossible to obtain fully-observed samples, but economical to obtain partial, noisy samples.
  • 9. Limitation of GAN • Training Stability • Forgetting problem • Requires Good (or fully observed) Training Samples!! • To train the generator, we need a lot of good images • In many settings, it is expensive or even impossible to obtain fully-observed samples, but economical to obtain partial, noisy samples. Compressed sensing attempts to address this problem by exploiting the models of the data structure; sparsity. Bora et al. ICML 2017, “Compressed Sensing using Generative Models”
  • 10. https://www.cs.utexas.edu/~ashishb/assets/csgm/slides_google_ashish.pdf Bora et al. 2017, “Compressed Sensing using Generative Models” Compressed Sensing Paper in “a” Slide
  • 11. Chicken and Egg problem • Okay, they showed it is possible to solve the problem with a small number of (good) measurements by using Generative Models! • But, what if it is even not possible to gather the good data in the first place? • How can we collect enough data to train a generative model to start with?
  • 15. Measurements? (in this paper) • Block-Pixels: with 𝑝, make image pixel 0 • Conv + Noise: Gaussian kernel blur + add random Gaussian noise • Block-Patch: make 𝑘 × 𝑘 patch 0 • Keep-Patch: make 0 except 𝑘 × 𝑘 patch • Extract-Patch: use 𝑘 × 𝑘 patch as input • Pad-Rotate-Project: zero padding + rotate + vertical 1D projection • Pad-Rotate-Project-𝛉: Pad-Rotate-Project and include θ as an additional information for the measurement • Gaussian-Projection: Sample a random Gaussian vector and project
  • 16. Dataset and Model Architectures
  • 25. In Conclusion • Using AmbientGAN, it is possible to train the generator without the fully-observed data • In theory, it is possible to find the true distribution by training the generator when the measurement process is invertible and differentiable • Empirically, it is possible to recover the good data distribution even though the measurement process is not clearly known.
  • 26. Possible Applications? • OCR • Gayoung’s Webtoon data • Adding Reconstruction loss and Cyclic loss • Learnable 𝒇 𝜽 −𝟏 ⋅ by FC • etc.