Generative adversarial networks (GANs) have shown remarkable success in generation of data from natural data manifolds such as images. In several scenarios, it is desirable that generated data is well-clustered, especially when there is severe class imbalance. In this paper, we focus on the problem of clustering in generated space of GANs and uncover its relationship with the characteristics of the latent space. We derive from first principles, the necessary and sufficient conditions needed to achieve faithful clustering in the GAN framework: (i) presence of a multimodal latent space with adjustable priors, (ii) existence of a latent space inversion mechanism and (iii) imposition of the desired cluster priors on the latent space. We also identify the GAN models in the literature that partially satisfy these conditions and demonstrate the importance of all the components required, through ablative studies on multiple real world image datasets. Additionally, we describe a procedure to construct a multimodal latent space which facilitates learning of cluster priors with sparse supervision.

本文探讨了生成对抗网络中聚类的问题，并从多模态潜在空间的角度提出了新的解决方案，该方案通过可调节的先验和所需聚类先验的影响来实现聚类。我们证明了所有组件的重要性，并提出了一种使用稀疏监控的多模态潜在空间的过程来便于学习聚类先验。

深度生成对抗网络中的潜在向量对聚类效果的影响