Semi-supervised learning (SSL) seeks to enhance task performance by training on both labeled and unlabeled data. Mainstream SSL image classification methods mostly optimize a loss that additively combines a supervised classification objective with a regularization term derived solely from unlabeled data. This formulation neglects the potential for interaction between labeled and unlabeled images. In this paper, we introduce InterLUDE, a new approach to enhance SSL made of two parts that each benefit from labeled-unlabeled interaction. The first part, embedding fusion, interpolates between labeled and unlabeled embeddings to improve representation learning. The second part is a new loss, grounded in the principle of consistency regularization, that aims to minimize discrepancies in the model's predictions between labeled versus unlabeled inputs. Experiments on standard closed-set SSL benchmarks and a medical SSL task with an uncurated unlabeled set show clear benefits to our approach. On the STL-10 dataset with only 40 labels, InterLUDE achieves 3.2% error rate, while the best previous method reports 14.9%.

本研究提出了一种新的半监督学习方法InterLUDE，通过嵌入融合和一种新的损失函数（基于一致性正则化原则），在标记和未标记数据之间实现相互作用，以提高SSL图像分类的性能。实验结果表明，InterLUDE在标准闭集SSL基准测试和医学SSL任务中表现出明显的优势。在只有40个标签的STL-10数据集上，InterLUDE的错误率为3.2％，而最佳先前方法的错误率为14.9％。

InterLUDE：标记数据与未标记数据之间的交互以增强半监督学习