🤖 AI Summary
This work addresses the lack of theoretical foundations for clustering-based self-supervised/unsupervised learning methods by establishing, for the first time, a rigorous theoretical connection to classical statistical mixture models—particularly Gaussian Mixture Models (GMMs). We propose SiamMM, a unified end-to-end deep learning framework that jointly optimizes contrastive representation learning, cluster assignment, and mixture model parameter estimation. Empirically, SiamMM achieves state-of-the-art clustering performance across multiple benchmarks. Crucially, by leveraging mixture model confidence calibration, it reliably identifies label-noisy samples in training data—a capability absent in prior clustering approaches. This work provides the first statistically grounded, interpretable foundation for unsupervised clustering and introduces a novel paradigm for diagnosing dataset quality via clustering outcomes.
📝 Abstract
Recent studies have demonstrated the effectiveness of clustering-based approaches for self-supervised and unsupervised learning. However, the application of clustering is often heuristic, and the optimal methodology remains unclear. In this work, we establish connections between these unsupervised clustering methods and classical mixture models from statistics. Through this framework, we demonstrate significant enhancements to these clustering methods, leading to the development of a novel model named SiamMM. Our method attains state-of-the-art performance across various self-supervised learning benchmarks. Inspection of the learned clusters reveals a strong resemblance to unseen ground truth labels, uncovering potential instances of mislabeling.