We propose a generative adversarial network with multiple discriminators, where each discriminator is specialized to distinguish the subset of a real dataset. This approach facilitates learning a generator coinciding with the underlying data distribution and thus mitigates the chronic mode collapse problem. From the inspiration of multiple choice learning, we guide each discriminator to have expertise in the subset of the entire data and allow the generator to find reasonable correspondences between the latent and real data spaces automatically without supervision for training examples and the number of discriminators. Despite the use of multiple discriminators, the backbone networks are shared across the discriminators and the increase of training cost is minimized. We demonstrate the effectiveness of our algorithm in the standard datasets using multiple evaluation metrics.

翻译：我们建议与多个歧视者建立具有多重歧视者的基因对抗网络,每个歧视者都专门区分真实数据集的子集,这有利于学习与基本数据分布相吻合的生成者,从而缓解长期模式崩溃问题。从多重选择学习的启发中,我们指导每个歧视者掌握全部数据子集的专门知识,并允许生成者在没有培训实例和歧视者人数监督的情况下,在潜在数据空间与实际数据空间之间自动找到合理的对应关系。尽管使用了多个歧视者,但主干网在歧视者之间共享,而培训成本的增加被降到最低。我们用多个评估指标来证明标准数据集中我们算法的有效性。