  Since their introduction in the last few years, conditional generative models
have seen remarkable achievements. However, they often need the use of large
amounts of labelled information. By using unsupervised conditional generation
in conjunction with a clustering inference network, ClusterGAN has recently
been able to achieve impressive clustering results. Since the real conditional
distribution of data is ignored, the clustering inference network can only
achieve inferior clustering performance by considering only uniform prior based
generative samples. However, the true distribution is not necessarily balanced.
Consequently, ClusterGAN fails to produce all modes, which results in
sub-optimal clustering inference network performance. So, it is important to
learn the prior, which tries to match the real distribution in an unsupervised
way. In this paper, we propose self-augmentation information maximization
improved ClusterGAN (SIMI-ClusterGAN) to learn the distinctive priors from the
data directly. The proposed SIMI-ClusterGAN consists of four deep neural
networks: self-augmentation prior network, generator, discriminator and
clustering inference network. The proposed method has been validated using
seven benchmark data sets and has shown improved performance over state-of-the
art methods. To demonstrate the superiority of SIMI-ClusterGAN performance on
imbalanced dataset, we have discussed two imbalanced conditions on MNIST
datasets with one-class imbalance and three classes imbalanced cases. The
results highlight the advantages of SIMI-ClusterGAN.

Machine Learning;Computer Vision and Pattern Recognition