Active Learning: Sampling in the Least Probable Disagreement Region
Active learning strategy to query samples closest to the decision boundary can be an effective strategy for sampling the most uncertain and thus informative samples. This strategy is valid only when the sample's "closeness" to the decision boundary can be estimated. As a measure for evaluating closeness to a given decision boundary of a given sample, this paper considers the least probable disagreement region (LPDR) which is a measure of the smallest perturbation on the decision boundary leading to altered prediction of the sample. Experimental results show that the proposed LPDR-based active learning algorithm consistently outperforms other high performing active learning algorithms and leads to state-of-the-art performance on various datasets and deep networks.
Code (0)
등록된 구현이 없습니다.
Tasks
Active LearningvalidSimilar Papers 제목 키워드 기반
Least Probable Disagreement Region for Active Learning
Active learning strategy to query unlabeled samples nearer the estimated decision boundary at each step has been known to be effective when the distance from the sample data to the decision boundary can be explicitly eva…
Active LearningImproved Algorithms for Agnostic Pool-based Active Classification
We consider active learning for binary classification in the agnostic pool-based setting. The vast majority of works in active learning in the agnostic setting are inspired by the CAL algorithm where each query is unifor…
Active LearningBinary ClassificationClassificationExperimental Design+2Disagreement-Regularized Importance Sampling for Adversarial Label Corruption
Standard Importance Sampling (IS) collapses under label corruption because high-norm examples, prioritized for variance reduction, are often adversarial outliers. We formalize this misalignment using an $\varepsilon$-con…
Bayesian Active Learning By Distribution Disagreement
Active Learning (AL) for regression has been systematically under-researched due to the increased difficulty of measuring uncertainty in regression models. Since normalizing flows offer a full predictive distribution ins…
Active LearningregressionUncertainty QuantificationBidirectional Uncertainty-Based Active Learning for Open Set Annotation
Active learning (AL) in open set scenarios presents a novel challenge of identifying the most valuable examples in an unlabeled data pool that comprises data from both known and unknown classes. Traditional methods prior…
Active Learning