Abstract
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 often 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 obtains 6.3%.
Recommended Citation
Z. Huang et al., "InterLUDE: Interactions Between Labeled And Unlabeled Data To Enhance Semi-Supervised Learning," Proceedings of Machine Learning Research, vol. 235, pp. 20452 - 20473, arXiv, Jan 2024.
The definitive version is available at https://doi.org/10.48550/arXiv.2403.10658
Department(s)
Computer Science
International Standard Serial Number (ISSN)
2640-3498
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2025 arXiv, All rights reserved.
Publication Date
01 Jan 2024
