Abstract
Image clustering has recently attracted significant attention due to the increased availability of unlabeled 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 to the input to an auxiliary classifier. An objective function is utilized to train the auxiliary classifier by maximizing the mutual information between the representations obtained via the discriminator model and the same representations perturbed via adversarial training. We further improve the robustness of the auxiliary classifier by introducing a penalty term into the objective function. This minimizes the divergence across multiple transformed representations generated by the discriminator model with a low dropout rate. 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 achieves competitive results compared with state-of-the-art clustering methods on a wide range of benchmark datasets including CIFAR-10, CIFAR-100/20, and STL10.