# An Efficient Explorative Sampling Considering the Generative Boundaries of Deep Generative Neural Networks

arXiv: https://arxiv.org/abs/1912.05827 · HTML: https://mlsanity.com/paper/1912.05827/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Dec 2019.

## Built on by (most cited first, 6 of 6)

- [Automatic Correction of Internal Units in Generative Neural Networks](https://mlsanity.com/paper/2104.06118/) (2021)
- [Learning Disentangled Semantic Spaces of Explanations via Invertible Neural Networks](https://mlsanity.com/paper/2305.01713/) (2023)
- [Quasi-symbolic Semantic Geometry over Transformer-based Variational AutoEncoder](https://mlsanity.com/paper/2210.06230/) (2022)
- [An Unsupervised Way to Understand Artifact Generating Internal Units in Generative Neural Networks](https://mlsanity.com/paper/2112.08814/) (2021)
- [Understanding Distributed Representations of Concepts in Deep Neural Networks without Supervision](https://mlsanity.com/paper/2312.17285/) (2023)
- [Explaining the Decisions of Deep Policy Networks for Robotic Manipulations](https://mlsanity.com/paper/2310.19432/) (2023)

## Abstract (arXiv)

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 ε-based sampling method.