Improving Multi-Label Contrastive Learning by Leveraging Label Distribution
In multi-label learning, leveraging contrastive learning to learn better representations faces a key challenge: selecting positive and negative samples and effectively utilizing label information. Previous studies selected positive and negative samples based on the overlap between labels and used them for label-wise loss balancing. However, these methods suffer from a complex selection process and fail to account for the varying importance of different labels. To address these problems, we propose a novel method that improves multi-label contrastive learning through label distribution. Specifically, when selecting positive and negative samples, we only need to consider whether there is an intersection between labels. To model the relationships between labels, we introduce two methods to recover label distributions from logical labels, based on Radial Basis Function (RBF) and contrastive loss, respectively. We evaluate our method on nine widely used multi-label datasets, including image and vector datasets. The results demonstrate that our method outperforms state-of-the-art methods in six evaluation metrics.
Code (0)
등록된 구현이 없습니다.
Tasks
Contrastive LearningMulti-Label LearningMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Unleashing the Potential of Open-set Noisy Samples Against Label Noise for Medical Image Classification
Addressing mixed closed-set and open-set label noise in medical image classification remains a largely unexplored challenge. Unlike natural image classification, which often separates and processes closed-set and open-se…
Classificationimage-classificationImage ClassificationMedical Image ClassificationDiCaP: Distribution-Calibrated Pseudo-labeling for Semi-Supervised Multi-Label Learning
Semi-supervised multi-label learning (SSMLL) aims to address the challenge of limited labeled data in multi-label learning (MLL) by leveraging unlabeled data to improve the model's performance. While pseudo-labeling has …
Multi-Label LearningContrastive LearningGeneralized Supervised Contrastive Learning
With the recent promising results of contrastive learning in the self-supervised learning paradigm, supervised contrastive learning has successfully extended these contrastive approaches to supervised contexts, outperfor…
Contrastive LearningKnowledge DistillationRepresentation LearningSelf-Supervised LearningIntegrating Distribution Matching into Semi-Supervised Contrastive Learning for Labeled and Unlabeled Data
The advancement of deep learning has greatly improved supervised image classification. However, labeling data is costly, prompting research into unsupervised learning methods such as contrastive learning. In real-world s…
Contrastive LearningImage ClassificationCollaborative Feature-Logits Contrastive Learning for Open-Set Semi-Supervised Object Detection
Current Semi-Supervised Object Detection (SSOD) methods enhance detector performance by leveraging large amounts of unlabeled data, assuming that both labeled and unlabeled data share the same label space. However, in op…
Contrastive Learningobject-detectionObject DetectionSemi-Supervised Object Detection