A GOODNESS OF FIT MEASURE FOR GENERATIVE NETWORKS

Lorenzo Luzi, Randall Balestriero, Richard Baraniuk

Abstract:

We define a goodness of fit measure for generative networks which captures how well the network can generate the training data, which is necessary to learn the true data distribution.
We demonstrate how our measure can be leveraged to understand mode collapse in generative adversarial networks and provide practitioners with a novel way to perform model comparison and early stopping without having to access another trained model as with Frechet Inception Distance or Inception Score. This measure shows that several successful, popular generative models, such as DCGAN and WGAN, fall very short of learning the data distribution. We identify this issue in generative models and empirically show that overparameterization via subsampling data and using a mixture of models improves performance in terms of goodness of fit.

Keywords: generative adversarial networks, goodness of fit, inception score, empirical approximation error, validation metric, frechet inception score