Triple Generative Adversarial Networks

By Chongxuan Li, Kun Xu, Jiashuo Liu, Jun Zhu, Bo Zhang. 2019

57 citations recorded by the source corpus.

We propose a unified game-theoretical framework to perform classification and conditional image generation given limited supervision. It is formulated as a three-player minimax game consisting of a generator, a classifier and a discriminator, and therefore is referred to as Triple Generative Adversarial Network (Triple-GAN). The generator and the classifier characterize the conditional distributions between images and labels to perform conditional generation and classification, respectively. The discriminator solely focuses on identifying fake image-label pairs. Theoretically, the three-player formulation guarantees consistency. Namely, under a nonparametric assumption, the unique equilibrium of the game is that the distributions characterized by the generator and the classifier converge to the data distribution. As a byproduct of the three-player formulation, Triple-GAN is flexible to incorporate different semi-supervised classifiers and GAN architectures. We evaluate Triple-GAN in two challenging settings, namely, semi-supervised learning and the extremely low data regime. In both settings, Triple-GAN can achieve excellent classification results and generate meaningful samples in a specific class simultaneously. In particular, using a commonly adopted 13-layer CNN classifier, Triple-GAN outperforms extensive semi-supervised learning methods substantially on several benchmarks no matter data augmentation is applied or not.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Jun Zhu
2. Liyuan Wang
3. Chongxuan Li
4. Lanqing Hong
5. Zhenguo Li
6. Kuo Yang
7. Hang Su
8. Xingxing Zhang

In Computer Vision and Pattern Recognition

1. Jun Zhu
2. Liyuan Wang
3. Chongxuan Li
4. Lanqing Hong
5. Zhenguo Li
6. Kuo Yang
7. Hang Su
8. Xingxing Zhang

Citations in the corpus

• ORDisCo: Effective and Efficient Usage of Incremental Unlabeled Data for Semi-supervised Continual Learning (2021, methodology): “4, the predictions on the conditional samples of ORDisCo are significantly better than the one of a sequentially trained conditional GAN with SMB.”