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Found 5 papers, 3 papers with code
Confident Anchor-Induced Multi-Source Free Domain Adaptation
To address these challenges, we develop a novel Confident-Anchor-induced multi-source-free Domain Adaptation (CAiDA) model, which is a pioneer exploration of knowledge adaptation from multiple source domains to the unlabeled target domain without any source data, but with only pre-trained source models.
Learning Bounds for Open-Set Learning
In this paper, we target a more challenging and realistic setting: open-set learning (OSL), where there exist test samples from the classes that are unseen during training.
Concept Drift Detection: Dealing with MissingValues via Fuzzy Distance Estimations
Our solution comprises a novel masked distance learning (MDL) algorithm to reduce the cumulative errors caused by iteratively estimating each missing value in an observation and a fuzzy-weighted frequency (FWF) method for identifying discrepancies in the data distribution.
Diverse Instances-Weighting Ensemble based on Region Drift Disagreement for Concept Drift Adaptation
Concept drift refers to changes in the distribution of underlying data and is an inherent property of evolving data streams.
Learning under Concept Drift: A Review
To help researchers identify which research topics are significant and how to apply related techniques in data analysis tasks, it is necessary that a high quality, instructive review of current research developments and trends in the concept drift field is conducted.
