InterLUDE: Interactions between Labeled and Unlabeled Data to Enhance Semi-Supervised Learning

• URL: http://arxiv.org/abs/2403.10658v1
• Date: Fri, 15 Mar 2024 19:54:10 GMT
• Title: InterLUDE: Interactions between Labeled and Unlabeled Data to Enhance Semi-Supervised Learning
• Authors: Zhe Huang, Xiaowei Yu, Dajiang Zhu, Michael C. Hughes,
• Abstract summary: Semi-supervised learning (SSL) seeks to enhance task performance by training on both labeled and unlabeled data. In this paper, we introduce InterLUDE, a new approach to enhance SSL made of two parts that each benefit from labeled-unlabeled interaction.
• Score: 13.010558192697133
• License: http://creativecommons.org/licenses/by/4.0/
• 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 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%.

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