Auxiliary Classifier GANs (AC-GANs) are widely used conditional generative
models and are capable of generating high-quality images. Previous work has
pointed out that AC-GAN learns a biased distribution. To remedy this, Twin
Auxiliary Classifier GAN (TAC-GAN) introduces a twin classifier to the min-max
game. However, it has been reported that using a twin auxiliary classifier may
cause instability in training. To this end, we propose an Unbiased Auxiliary
GANs (UAC-GAN) that utilizes the Mutual Information Neural Estimator (MINE) to
estimate the mutual information between the generated data distribution and
labels. To further improve the performance, we also propose a novel
projection-based statistics network architecture for MINE. Experimental results
on three datasets, including Mixture of Gaussian (MoG), MNIST and CIFAR10
datasets, show that our UAC-GAN performs better than AC-GAN and TAC-GAN. Code
can be found on the project website.

本研究提出了一种无偏的辅助生成对抗网络 (UAC-GAN)，采用 Mutual Information Neural Estimator (MINE) 评估生成数据分布与标签之间的互信息，并基于投影的统计网络结构进一步提高了性能，在三个数据集上展示出比 AC-GAN 和 TAC-GAN 更好的表现。