Training disentangled representations with generative adversarial networks (GANs) remains challenging, with leading implementations failing to achieve comparable performance to Variational Autoencoder (VAE)-based methods. After $\beta$-VAE and FactorVAE discovered that regularizing the total correlation of the latent vectors promotes disentanglement, numerous VAE-based methods emerged. Such a discovery has yet to be made for GANs, and reported disentanglement scores of GAN-based methods are significantly inferior to VAE-based methods on benchmark datasets. To this end, we propose a novel regularizer that achieves higher disentanglement scores than state-of-the-art VAE- and GAN-based approaches. The proposed contrastive regularizer is inspired by a natural notion of disentanglement: latent traversal. Latent traversal refers to generating images by varying one latent code while fixing the rest. We turn this intuition into a regularizer by adding a discriminator that detects how the latent codes are coupled together, in paired examples. Numerical experiments show that this approach improves upon competing state-of-the-art approaches on benchmark datasets.

通过使用自监督学习的方法一方面设计了一种新的对抗网络（GAN）模型和一个无监督模型筛选方案，另一方面提出了一种对比约束规则来提高模型向目标空间映射时对潜在因素进行解耦的能力。这种方案与对抗网络或自编码器等其他方法相比，具有更高的可解释性和速度。

InfoGAN-CR 和 ModelCentrality: 面向 GAN 解耦的自监督模型训练和选择