  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%.
