Abstract: To address the issue of negative-positive coupling between positive and negative samples in contrastive clustering, a decoupled contrastive clustering integrating attention mechanism (DCCIAM) is proposed. Firstly, data augmentation techniques are employed to expand the image data to obtain positive and negative sample pairs. Secondly, a convolutional block attention module (CBAM) is integrated into the backbone network to make the network pay more attention to target features. The expanded image data is then input into the backbone network to obtain a feature. Subsequently, the featurespassed through a neural network projection head to calculate instance loss and clustering loss separately. Finally, feature representation and cluster assignment are performed by combining the instance loss and clustering loss. To validate the effectiveness of the DCCIAM method, experiments are conducted on public image datasets CIFAR-10, STL-10, and ImageNet-10, achieving clustering accuracies of 80.2%, 77.0%, and 90.4%, respectively. The results demonstrate that the decoupled contrastive clustering method integrated with an attention mechanism performs well in image clustering.

Key words: contrastive learning, decoupled contrastive loss, convolutional attention module, image clustering, data augmentation