  Disentanglement, a critical concern in interpretable machine learning, has
also garnered significant attention from the computer vision community. Many
existing GAN-based class disentanglement (unsupervised) approaches, such as
InfoGAN and its variants, primarily aim to maximize the mutual information (MI)
between the generated image and its latent codes. However, this focus may lead
to a tendency for the network to generate highly similar images when presented
with the same latent class factor, potentially resulting in mode collapse or
mode dropping. To alleviate this problem, we propose \texttt{CoDeGAN}
(Contrastive Disentanglement for Generative Adversarial Networks), where we
relax similarity constraints for disentanglement from the image domain to the
feature domain. This modification not only enhances the stability of GAN
training but also improves their disentangling capabilities. Moreover, we
integrate self-supervised pre-training into CoDeGAN to learn semantic
representations, significantly facilitating unsupervised disentanglement.
Extensive experimental results demonstrate the superiority of our method over
state-of-the-art approaches across multiple benchmarks. The code is available
at https://github.com/learninginvision/CoDeGAN.
