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Generative Modeling by Estimating Gradients of the Data Distribution Yang Song, Stefano Ermon David Zimmerer Medical Image Analysis ( #MIA-san-mia ) DKFZ Author Division Generative modeling 02.11.16 | Page2 2 | Author Division

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Generative Modeling by Estimating Gradients of the Data Distribution

David Zimmerer Medical Image Analysis ( #MIA-san-mia ) DKFZ Yang Song, Stefano Ermon

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Generative modeling

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Generative modeling

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Generative modeling

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Generative modeling

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Generative modeling

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Generative modeling

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Generative modeling

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“New” Idea: Generative Modeling by Estimating Gradients of the Data Distribution

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“New” Idea: Generative Modeling by Estimating Gradients of the Data Distribution

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“New” Idea: Generative Modeling by Estimating Gradients of the Data Distribution

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“New” Idea: Generative Modeling by Estimating Gradients of the Data Distribution

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Score matching

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[1]

A. Hyvärinen. Estimation of non-normalized statistical models by score matching. Journal of

Machine Learning Research, 6(Apr):695–709, 2005.

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Score matching

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[1]

A. Hyvärinen. Estimation of non-normalized statistical models by score matching. Journal of

Machine Learning Research, 6(Apr):695–709, 2005.

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Score matching

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→

[1]

A. Hyvärinen. Estimation of non-normalized statistical models by score matching. Journal of

Machine Learning Research, 6(Apr):695–709, 2005.

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Score matching

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→

[1]

A. Hyvärinen. Estimation of non-normalized statistical models by score matching. Journal of

Machine Learning Research, 6(Apr):695–709, 2005.

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Score matching

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→ →

[1]

A. Hyvärinen. Estimation of non-normalized statistical models by score matching. Journal of

Machine Learning Research, 6(Apr):695–709, 2005.

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Trivial Implementation (on MNIST)

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Trivial Implementation (on MNIST)

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Trivial Implementation (on MNIST)

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Spoiler: Improved Results

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Spoiler: Improved Results

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Spoiler: Improved Results

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Spoiler: Improved Results

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What are the problems ?

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What are the problems ?

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What are the problems ?

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What are the problems ?

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What are the problems ?

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What are the problems ?

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What are the problems ?

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How to sample:

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How to sample:

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How to sample:

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How to sample:

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How to sample:

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How to sample:

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Approach

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Approach

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Results

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Results: Qualitative

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Results: Qualitative

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Results: Quantitative

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Results: Reproducible

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[2]

A. Matosevic. Reproducibility Challenge – Generative Modeling by Estimating Gradients of the Data Distribution,

NeurIPS 2019 Reproducibility Challenge Blind Report, https://openreview.net/forum?id=SkxCSTqG6H

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Results: Reproducible

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[2]

A. Matosevic. Reproducibility Challenge – Generative Modeling by Estimating Gradients of the Data Distribution,

NeurIPS 2019 Reproducibility Challenge Blind Report, https://openreview.net/forum?id=SkxCSTqG6H

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Results: Reproducible

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[2]

A. Matosevic. Reproducibility Challenge – Generative Modeling by Estimating Gradients of the Data Distribution,

NeurIPS 2019 Reproducibility Challenge Blind Report, https://openreview.net/forum?id=SkxCSTqG6H

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Results: Reproducible

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→

[2]

A. Matosevic. Reproducibility Challenge – Generative Modeling by Estimating Gradients of the Data Distribution,

NeurIPS 2019 Reproducibility Challenge Blind Report, https://openreview.net/forum?id=SkxCSTqG6H

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Results: Reproducible

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→

[2]

A. Matosevic. Reproducibility Challenge – Generative Modeling by Estimating Gradients of the Data Distribution,

NeurIPS 2019 Reproducibility Challenge Blind Report, https://openreview.net/forum?id=SkxCSTqG6H

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The End

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