CCEGAN: Enhancing GAN clustering through contrastive clustering ensemble

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Clustering algorithms play a crucial role in various domains, and recent advancements in Generative Adversarial Network (GAN) techniques have opened new possibilities for improving clustering effectiveness. This paper aims to enhance the performance of GAN clustering by addressing the challenge of generating high-quality labeled samples. We propose a novel contrastive network and a voting-based method to progressively filter and fuse information from synthetic samples. These methods are incorporated into a deep clustering ensemble framework, which combines the advantages of GAN clustering and ensemble learning. Through comprehensive empirical analysis on diverse datasets, including both image and non-image datasets, we demonstrate the superiority of our proposed method in terms of effectiveness and robustness. Our approach outperforms existing GAN clustering methods while maintaining a reasonable computational time. This work contributes to the field of clustering algorithms by providing a more effective and robust approach for leveraging GANs in the clustering process. The code is available at https://github.com/Jarvisyan/CCEGAN-pytorch.