  Deep generative neural networks (DGNNs) have achieved realistic and
high-quality data generation. In particular, the adversarial training scheme
has been applied to many DGNNs and has exhibited powerful performance. Despite
of recent advances in generative networks, identifying the image generation
mechanism still remains challenging. In this paper, we present an explorative
sampling algorithm to analyze generation mechanism of DGNNs. Our method
efficiently obtains samples with identical attributes from a query image in a
perspective of the trained model. We define generative boundaries which
determine the activation of nodes in the internal layer and probe inside the
model with this information. To handle a large number of boundaries, we obtain
the essential set of boundaries using optimization. By gathering samples within
the region surrounded by generative boundaries, we can empirically reveal the
characteristics of the internal layers of DGNNs. We also demonstrate that our
algorithm can find more homogeneous, the model specific samples compared to the
variations of {\epsilon}-based sampling method.
