# RankGAN: A Maximum Margin Ranking GAN for Generating Faces

arXiv: https://arxiv.org/abs/1812.08196 · HTML: https://mlsanity.com/paper/1812.08196/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Dec 2018.

## Built on by (most cited first, 4 of 4)

- [A Review of Deep Learning for Video Captioning](https://mlsanity.com/paper/2304.11431/) (2023)
- [JPGNet: Joint Predictive Filtering and Generative Network for Image Inpainting](https://mlsanity.com/paper/2107.04281/) (2021)
- [3DFaceFill: An Analysis-By-Synthesis Approach to Face Completion](https://mlsanity.com/paper/2110.10395/) (2021)
- [PhysioGAN: Training High Fidelity Generative Model for Physiological Sensor Readings](https://mlsanity.com/paper/2204.13597/) (2022)

## Abstract (arXiv)

We present a new stage-wise learning paradigm for training generative adversarial networks (GANs). The goal of our work is to progressively strengthen the discriminator and thus, the generators, with each subsequent stage without changing the network architecture. We call this proposed method the RankGAN. We first propose a margin-based loss for the GAN discriminator. We then extend it to a margin-based ranking loss to train the multiple stages of RankGAN. We focus on face images from the CelebA dataset in our work and show visual as well as quantitative improvements in face generation and completion tasks over other GAN approaches, including WGAN and LSGAN.