InterLUDE: Interactions between Labeled and Unlabeled Data to Enhance Semi-Supervised Learning
<|reference_start|>InterLUDE: Interactions between Labeled and Unlabeled Data to Enhance Semi-Supervised Learning: 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%.<|reference_end|>
@article{huang2024interlude:,
    title={InterLUDE: Interactions between Labeled and Unlabeled Data to Enhance
  Semi-Supervised Learning},
    author={Zhe Huang, Xiaowei Yu, Dajiang Zhu, Michael C. Hughes},
    journal={arXiv preprint arXiv:2403.10658},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.10658},
    primaryClass={cs.CV cs.LG}
}