# Pipeline Generative Adversarial Networks for Facial Images Generation with Multiple Attributes

arXiv: https://arxiv.org/abs/1711.10742 · HTML: https://mlsanity.com/paper/1711.10742/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Nov 2017.

## Built on by (most cited first, 1 of 1)

- [Testing the Channels of Convolutional Neural Networks](https://mlsanity.com/paper/2303.03400/) (2023)

## Abstract (arXiv)

Generative Adversarial Networks are proved to be efficient on various kinds of image generation tasks. However, it is still a challenge if we want to generate images precisely. Many researchers focus on how to generate images with one attribute. But image generation under multiple attributes is still a tough work. In this paper, we try to generate a variety of face images under multiple constraints using a pipeline process. The Pip-GAN (Pipeline Generative Adversarial Network) we present employs a pipeline network structure which can generate a complex facial image step by step using a neutral face image. We applied our method on two face image databases and demonstrate its ability to generate convincing novel images of unseen identities under multiple conditions previously.