  Training generative models like Generative Adversarial Network (GAN) is
challenging for noisy data. A novel curriculum learning algorithm pertaining to
clustering is proposed to address this issue in this paper. The curriculum
construction is based on the centrality of underlying clusters in data points.
The data points of high centrality takes priority of being fed into generative
models during training. To make our algorithm scalable to large-scale data, the
active set is devised, in the sense that every round of training proceeds only
on an active subset containing a small fraction of already trained data and the
incremental data of lower centrality. Moreover, the geometric analysis is
presented to interpret the necessity of cluster curriculum for generative
models. The experiments on cat and human-face data validate that our algorithm
is able to learn the optimal generative models (e.g. ProGAN) with respect to
specified quality metrics for noisy data. An interesting finding is that the
optimal cluster curriculum is closely related to the critical point of the
geometric percolation process formulated in the paper.
