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％。