  Image generation has been heavily investigated in computer vision, where one
core research challenge is to generate images from arbitrarily complex
distributions with little supervision. Generative Adversarial Networks (GANs)
as an implicit approach have achieved great successes in this direction and
therefore been employed widely. However, GANs are known to suffer from issues
such as mode collapse, non-structured latent space, being unable to compute
likelihoods, etc. In this paper, we propose a new unsupervised non-parametric
method named mixture of infinite conditional GANs or MIC-GANs, to tackle
several GAN issues together, aiming for image generation with parsimonious
prior knowledge. Through comprehensive evaluations across different datasets,
we show that MIC-GANs are effective in structuring the latent space and
avoiding mode collapse, and outperform state-of-the-art methods. MICGANs are
adaptive, versatile, and robust. They offer a promising solution to several
well-known GAN issues. Code available: github.com/yinghdb/MICGANs.
