@article{Djolonga2017LearningIG, title={Learning Implicit Generative Models Using Differentiable Graph Tests}, author={Josip Djolonga and Andreas Krause}, journal={CoRR}, year={2017}, volume={abs/1709.01006} }

- Published 2017 in ArXiv

Recently, there has been a growing interest in the problem of learning rich implicit models — those from which we can sample, but can not evaluate their density. These models apply some parametric function, such as a deep network, to a base measure, and are learned end-to-end using stochastic optimization. One strategy of devising a loss function is through the statistics of two sample tests — if we can fool a statistical test, the learned distribution should be a good model of the true data… CONTINUE READING