Contrastive Clustering (2009.09687v1)

Abstract: In this paper, we propose a one-stage online clustering method called Contrastive Clustering (CC) which explicitly performs the instance- and cluster-level contrastive learning. To be specific, for a given dataset, the positive and negative instance pairs are constructed through data augmentations and then projected into a feature space. Therein, the instance- and cluster-level contrastive learning are respectively conducted in the row and column space by maximizing the similarities of positive pairs while minimizing those of negative ones. Our key observation is that the rows of the feature matrix could be regarded as soft labels of instances, and accordingly the columns could be further regarded as cluster representations. By simultaneously optimizing the instance- and cluster-level contrastive loss, the model jointly learns representations and cluster assignments in an end-to-end manner. Extensive experimental results show that CC remarkably outperforms 17 competitive clustering methods on six challenging image benchmarks. In particular, CC achieves an NMI of 0.705 (0.431) on the CIFAR-10 (CIFAR-100) dataset, which is an up to 19\% (39\%) performance improvement compared with the best baseline.

• The paper presents a novel dual contrastive learning framework that simultaneously optimizes instance and cluster representations.
• It achieves superior performance on six image datasets, with improvements up to 39% in Normalized Mutual Information over prior baselines.
• The one-stage, end-to-end model enables real-time, large-scale clustering, setting the stage for advanced unsupervised learning applications.

Contrastive Clustering: A Novel Approach to Unsupervised Image Clustering

In a significant contribution to the field of unsupervised learning and clustering, the paper titled "Contrastive Clustering" presents a one-stage online clustering method identified as Contrastive Clustering (CC). This approach fundamentally integrates instance- and cluster-level contrastive learning, introducing an innovative methodology to improve clustering performance on complex datasets.

Overview of Methodology

CC leverages a dual contrastive learning framework, where instance and cluster representations are learned simultaneously in an end-to-end manner. The main insight of this approach is interpreting the rows and columns of a feature matrix as soft labels of instances and cluster representations, respectively. This perspective allows the concurrent optimization of instance- and cluster-level contrastive loss, facilitating the joint learning of representations and cluster assignments.

For a given dataset, positive and negative instance pairs are created via data augmentations and are projected into a feature space. The instance-level contrastive learning occurs in the row space, and the cluster-level contrastive learning transpires in the column space. This dual approach helps the model maximize the similarities of positive pairs while minimizing those of negative ones.

Performance Evaluation

The proposed CC exhibits superior performance across six challenging datasets, which include CIFAR-10, CIFAR-100, STL-10, ImageNet-10, ImageNet-Dogs, and Tiny-ImageNet. Noteworthy results include achieving a Normalized Mutual Information (NMI) of 0.705 on CIFAR-10 and 0.431 on CIFAR-100, marking a significant improvement of up to 19% and 39% respectively over the best existing baselines.

Technical Contributions

1. Novel Dual Contrastive Learning Framework: The method unifies deep clustering within the broader framework of representation learning by aligning instance representation and clustering prediction with the row and column of a learnable feature matrix.
2. Task-Specific Contrastive Learning: Unlike existing contrastive learning models that focus on general representation, the proposed methodology is specifically tailored for clustering tasks, integrating both instance- and cluster-level contrastive learning.
3. One-Stage End-to-End Optimized Model: The approach functions in a dynamic, batch-wise optimization environment, making it suitable for large-scale and online scenarios without the common pitfalls of accumulated errors found in alternating iterative models.

Implications and Future Directions

The findings from this research have profound implications for the development of more efficient and robust clustering algorithms, particularly in scenarios requiring real-time or large-scale data processing. The dual-layer contrastive learning model not only enhances clustering tasks but also sets a framework that can be extended to other unsupervised learning paradigms, such as semi-supervised and transfer learning.

Future developments could explore the applicability of this method to non-image data or examine the integration of additional learning modalities (e.g., multi-view or multimodal learning) to enhance the flexibility and scalability of clustering approaches.

This paper undoubtedly contributes valuable insights and tools to the arsenal of unsupervised learning techniques, with potential applications extending beyond the immediate domain of image clustering to broader AI and data science challenges.