  Image clustering has recently attracted significant attention due to the
increased availability of unlabelled datasets. The efficiency of traditional
clustering algorithms heavily depends on the distance functions used and the
dimensionality of the features. Therefore, performance degradation is often
observed when tackling either unprocessed images or high-dimensional features
extracted from processed images. To deal with these challenges, we propose a
deep clustering framework consisting of a modified generative adversarial
network (GAN) and an auxiliary classifier. The modification employs Sobel
operations prior to the discriminator of the GAN to enhance the separability of
the learned features. The discriminator is then leveraged to generate
representations as the input to an auxiliary classifier. An adaptive objective
function is utilised to train the auxiliary classifier for clustering the
representations, aiming to increase the robustness by minimizing the divergence
of multiple representations generated by the discriminator. The auxiliary
classifier is implemented with a group of multiple cluster-heads, where a
tolerance hyper-parameter is used to tackle imbalanced data. Our results
indicate that the proposed method significantly outperforms state-of-the-art
clustering methods on CIFAR-10 and CIFAR-100, and is competitive on the STL10
and MNIST datasets.
