  Incomplete Multi-View Clustering aims to enhance clustering performance by
using data from multiple modalities. Despite the fact that several approaches
for studying this issue have been proposed, the following drawbacks still
persist: 1) It's difficult to learn latent representations that account for
complementarity yet consistency without using label information; 2) and thus
fails to take full advantage of the hidden information in incomplete data
results in suboptimal clustering performance when complete data is scarce. In
this paper, we propose Contrastive Incomplete Multi-View Image Clustering with
Generative Adversarial Networks (CIMIC-GAN), which uses GAN to fill in
incomplete data and uses double contrastive learning to learn consistency on
complete and incomplete data. More specifically, considering diversity and
complementary information among multiple modalities, we incorporate
autoencoding representation of complete and incomplete data into double
contrastive learning to achieve learning consistency. Integrating GANs into the
autoencoding process can not only take full advantage of new features of
incomplete data, but also better generalize the model in the presence of high
data missing rates. Experiments conducted on \textcolor{black}{four}
extensively-used datasets show that CIMIC-GAN outperforms state-of-the-art
incomplete multi-View clustering methods.
