SuperCM: Improving Semi-Supervised Learning and Domain Adaptation through differentiable clustering

• URL: http://arxiv.org/abs/2507.13779v1
• Date: Fri, 18 Jul 2025 09:42:39 GMT
• Title: SuperCM: Improving Semi-Supervised Learning and Domain Adaptation through differentiable clustering
• Authors: Durgesh Singh, Ahcène Boubekki, Robert Jenssen, Michael Kampffmeyer,
• Abstract summary: Semi-Supervised Learning (SSL) and Unsupervised Domain Adaptation (UDA) enhance the model performance by exploiting information from labeled and unlabeled data.<n>Recent works have utilized different training mechanisms to implicitly enforce the clustering assumption for the SSL and UDA.<n>We take a different approach by explicitly involving a differentiable clustering module which is extended to leverage the supervised data to compute its centroids.
• Score: 17.761439828914636
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Semi-Supervised Learning (SSL) and Unsupervised Domain Adaptation (UDA) enhance the model performance by exploiting information from labeled and unlabeled data. The clustering assumption has proven advantageous for learning with limited supervision and states that data points belonging to the same cluster in a high-dimensional space should be assigned to the same category. Recent works have utilized different training mechanisms to implicitly enforce this assumption for the SSL and UDA. In this work, we take a different approach by explicitly involving a differentiable clustering module which is extended to leverage the supervised data to compute its centroids. We demonstrate the effectiveness of our straightforward end-to-end training strategy for SSL and UDA over extensive experiments and highlight its benefits, especially in low supervision regimes, both as a standalone model and as a regularizer for existing approaches.

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