Probabilistic Matching of Real and Generated Data Statistics in Generative Adversarial Networks
This addresses the need for accurate distribution matching in GANs for scientific applications, though it is incremental as it builds on existing GAN frameworks.
The authors tackled the problem of ensuring generated data statistics match real data distributions in GANs, particularly for scientific applications, by adding a loss term based on f-divergences and kernel density estimation, and demonstrated improved performance on synthetic and real-world datasets.
Generative adversarial networks constitute a powerful approach to generative modeling. While generated samples often are indistinguishable from real data, there is no guarantee that they will follow the true data distribution. For scientific applications in particular, it is essential that the true distribution is well captured by the generated distribution. In this work, we propose a method to ensure that the distributions of certain generated data statistics coincide with the respective distributions of the real data. In order to achieve this, we add a new loss term to the generator loss function, which quantifies the difference between these distributions via suitable f-divergences. Kernel density estimation is employed to obtain representations of the true distributions, and to estimate the corresponding generated distributions from minibatch values at each iteration. When compared to other methods, our approach has the advantage that the complete shapes of the distributions are taken into account. We evaluate the method on a synthetic dataset and a real-world dataset and demonstrate improved performance of our approach.