  Mode collapse is a significant unsolved issue of generative adversarial
networks. In this work, we examine the causes of mode collapse from a novel
perspective. Due to the nonuniform sampling in the training process, some
sub-distributions may be missed when sampling data. As a result, even when the
generated distribution differs from the real one, the GAN objective can still
achieve the minimum. To address the issue, we propose a global distribution
fitting (GDF) method with a penalty term to confine the generated data
distribution. When the generated distribution differs from the real one, GDF
will make the objective harder to reach the minimal value, while the original
global minimum is not changed. To deal with the circumstance when the overall
real data is unreachable, we also propose a local distribution fitting (LDF)
method. Experiments on several benchmarks demonstrate the effectiveness and
competitive performance of GDF and LDF.
