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.

翻译：半监督学习（SSL）与无监督领域自适应（UDA）通过利用标注与未标注数据中的信息来提升模型性能。聚类假设已被证明对有限监督下的学习具有优势，其主张高维空间中属于同一簇的数据点应被分配至同一类别。近期研究采用不同的训练机制，在SSL与UDA中隐式地强化这一假设。本文提出一种不同的方法，通过显式引入可微分聚类模块，并扩展该模块以利用监督数据计算其聚类中心。我们通过大量实验验证了这种简洁的端到端训练策略在SSL与UDA中的有效性，并强调其优势——尤其在低监督条件下，既可作为独立模型使用，亦可作为现有方法的正则化器。