  Generative adversarial models are powerful tools to model structure in
complex distributions for a variety of tasks. Current techniques for learning
generative models require an access to samples which have high quality, and
advanced generative models are applied to generate samples from noisy training
data through ambient modules. However, the modules are only practical for the
output space of the generator, and their application in the hidden space is not
well studied. In this paper, we extend the ambient module to the hidden space
of the generator, and provide the uniqueness condition and the corresponding
strategy for the ambient hidden generator in the adversarial training process.
We report the practicality of the proposed method on the benchmark dataset.
