jankrepl / GAN-1D-distribution-fitting

Fitting 1D distribution with Generative Adversarial Networks

General Description

Approximating 1-D distributions through Generative Adversarial Networks.

Code

Running

python Main.py

Dependencies

• numpy
• scipy
• tensorflow

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Description

If our noise follows a uniform distribution on (0,1) we can think of the discriminators network as an approximator of the inverse cdf. Let U ~ Unifrom(0,1) and F be increasing cdf, then it holds F^{-1}(U) ~ F. Note that it can also learn a different mapping than inverse cdf.

Visualizations

Evolution of G approximator, density (histogram) and decision boundary where the true distribution is N(0,1)

• G (approximation of inverse cdf of standard normal)

• Decision boundary (probability of discriminator classifying a given point as real (=coming from true distribution))

• Histogram (pdf)

Conclusion and results

GANs are really sensitive to input parameters and the convergence is not guaranteed.

Problems

• convergence not guaranteed
• mode collapse (generator learns to fool the discriminator without approximating the true distirbution)

ToDos

• Minibatch discrimination (discriminator classifies batches rather than single numbers)

Sources

https://github.com/ericjang/genadv_tutorial

https://github.com/emsansone/GAN

http://blog.aylien.com/introduction-generative-adversarial-networks-code-tensorflow/