Contrastive deep convolutional transform k-means clustering - Goel, Anurag; Majumdar, Angshul (Information sciences, 2024)
Deep clustering has gained the immense attention of researchers in recent years. Most of the deep clustering approaches are based on auto-encoders which consist of an encoder-decoder framework. In these approaches, the clustering module is embedded in the latent space of auto-encoders. The auto-encoder based deep clustering approaches require learning of encoder weights as well as decoder weights. Moreover, due to the unsupervised learning strategy, these approaches lack in learning the discriminative features that can help in generating better clusters. This work introduces a novel clustering approach based on Contrastive Deep Convolutional Transform Learning (DCTL) framework. The proposed approach mitigates the problem of lack of supervision in DCTL based K-means clustering approach by embedding the contrastive learning into it. To embed the contrastive learning, the positive pairs and negative pairs of data samples are generated by reconstructing the data samples from the DCTL learnt representation itself and thus eliminates the requirement of data augmentation for embedding contrastive learning. The experimental results on several benchmark facial images datasets demonstrate that the proposed framework gives better clustering performance as compared to the current state-of-the-art deep clustering approaches especially in data constrained scenarios. •We propose a novel framework that can learn compact and discriminative representations for clustering.•The contrastive learning is embedded using a reconstruction module with skip connection.•Comparison with SOTA on several facial images datasets demonstrate the effectiveness of the proposed model.