Consistency-Based Semi-Supervised Active Learning: Towards Minimizing Labeling Budget
Active learning (AL) aims to integrate data labeling and model training in a unified way, and to minimize the labeling budget by prioritizing the selection of high value data that can best improve model performance. Readily-available unlabeled data are used to evaluate selection mechanisms, but are not used for model training in conventional pool-based AL. To minimize the labeling budget, we unify unlabeled sample selection and model training based on two principles. First, we exploit both labeled and unlabeled data using semi-supervised learning (SSL) to distill information from unlabeled data that improves representation learning and sample selection. Second, we propose a simple yet effective selection metric that is coherent with the training objective such that the selected samples are effective at improving model performance. Our experimental results demonstrate superior performance with our proposed principles for limited labeled data compared to alternative AL and SSL combinations. In addition, we study the AL phenomena of `cold start', which is becoming an increasingly more important factor to enable optimal unification of data labeling, model training and labeling budget minimization. We propose a measure that is found to be empirically correlated with the AL target loss. This measure can be used to assist in determining the proper start size.
Code (0)
등록된 구현이 없습니다.
Tasks
Active LearningRepresentation LearningSimilar Papers 제목 키워드 기반
Consistency-based Semi-supervised Active Learning: Towards Minimizing Labeling Cost
Active learning (AL) combines data labeling and model training to minimize the labeling cost by prioritizing the selection of high value data that can best improve model performance. In pool-based active learning, access…
Active Learningimage-classificationImage ClassificationRepresentation LearningNoise-injected Consistency Training and Entropy-constrained Pseudo Labeling for Semi-supervised Extractive Summarization
Labeling large amounts of extractive summarization data is often prohibitive expensive due to time, financial, and expertise constraints, which poses great challenges to incorporating summarization system in practical ap…
Extractive SummarizationExploiting Diversity of Unlabeled Data for Label-Efficient Semi-Supervised Active Learning
The availability of large labeled datasets is the key component for the success of deep learning. However, annotating labels on large datasets is generally time-consuming and expensive. Active learning is a research area…
Active LearningDiversityRepresentation LearningLabOR: Labeling Only if Required for Domain Adaptive Semantic Segmentation
Unsupervised Domain Adaptation (UDA) for semantic segmentation has been actively studied to mitigate the domain gap between label-rich source data and unlabeled target data. Despite these efforts, UDA still has a long wa…
Domain AdaptationSemantic SegmentationUnsupervised Domain AdaptationOn the Marginal Benefit of Active Learning: Does Self-Supervision Eat Its Cake?
Active learning is the set of techniques for intelligently labeling large unlabeled datasets to reduce the labeling effort. In parallel, recent developments in self-supervised and semi-supervised learning (S4L) provide p…
Active LearningContrastive LearningData Augmentation