Supercm: Revisiting Clustering for Semi-Supervised Learning

2025-06-30

AI Summary

Existing semi-supervised learning (SSL) methods often rely on complex consistency regularization or entropy minimization, making it challenging to exploit the cluster assumption in a simple and effective manner. This paper proposes an end-to-end differentiable clustering framework that explicitly models and incorporates the cluster assumption: supervised samples guide the dynamic optimization of learnable cluster centers, and the clustering module is jointly optimized with the supervised loss—eliminating the need for auxiliary regularization terms. The method is architecturally lightweight, plug-and-play, and seamlessly enhances mainstream SSL algorithms. Extensive experiments on standard benchmarks—including CIFAR-10/100 and SVHN—demonstrate substantial improvements over fully supervised baselines and consistent performance gains when integrated with state-of-the-art SSL methods such as FixMatch and UDA. These results validate both the effectiveness and generalizability of explicit clustering modeling in SSL.

Technology Category

• Machine Learning: Semi-Supervised Learning
• Search and Optimization: Learning to Search
• Computer Vision: Learning & Optimization for CV

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs

Abstract

The development of semi-supervised learning (SSL) has in recent years largely focused on the development of new consistency regularization or entropy minimization approaches, often resulting in models with complex training strategies to obtain the desired results. In this work, we instead propose a novel approach that explicitly incorporates the underlying clustering assumption in SSL through extending a recently proposed differentiable clustering module. Leveraging annotated data to guide the cluster centroids results in a simple end-to-end trainable deep SSL approach. We demonstrate that the proposed model improves the performance over the supervised-only baseline and show that our framework can be used in conjunction with other SSL methods to further boost their performance.

Problem

• Enhancing semi-supervised learning with clustering techniques
• Simplifying SSL models by integrating differentiable clustering
• Improving performance by combining clustering with existing SSL methods

Innovation

• Differentiable clustering module integration
• End-to-end trainable deep SSL
• Compatible with other SSL methods