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.

翻译：模式坍塌是对抗生成网络中一个尚未解决的重要问题。本文从全新视角探讨了模式坍塌的成因：由于训练过程中的非均匀采样，数据采样时可能遗漏部分子分布，导致即便生成分布与真实分布存在差异，GAN目标函数仍能达到最小值。针对该问题，我们提出了一种带有惩罚项的全局分布拟合（GDF）方法，通过约束生成数据分布来解决该问题。当生成分布与真实分布存在差异时，GDF会使目标函数更难达到最小值，同时保持原始全局最小值不变。针对无法获取完整真实数据的场景，我们还提出了局部分布拟合（LDF）方法。在多个基准数据集上的实验表明，GDF与LDF方法具有显著有效性和竞争性能。