From Local Geometry to Global Pseudo Labeling for Robust Positive Unlabeled Learning under Covariate Shift
arXiv:2605.31187v1 Announce Type: cross Abstract: Detecting covariate shift is critical for building reliable vision systems. While most prior work focuses on improving robustness to shift, explicitly detecting covariate shift remains underexplored. Existing approaches typically rely on fully supervised training, requiring labeled examples from both original and shifted distributions, which is often impractical. In this paper, we show that covariate shift detection can be effectively addressed with weaker supervision using Positive Unlabeled (PU) learning. However, under covariate shift, in di
The increasing deployment of AI systems in real-world scenarios necessitates more robust methods for detecting and adapting to shifts in data distribution without relying on extensive labeled data.
Improved detection of covariate shift with weaker supervision like Positive Unlabeled learning enhances the reliability and trustworthiness of AI systems, particularly in vision applications, making them more adaptable to changing environments.
AI models can potentially become more resilient and require less human intervention for adaptation when encountering new or shifted data distributions, broadening their applicable domains.
- · AI Vision Systems Developers
- · Autonomous Systems
- · Industries deploying AI in variable environments
- · Data scientists
- · Systems highly reliant on perfectly matched training data
- · Manual data labeling services for shift detection
More robust and adaptable AI models are developed, reducing field failures caused by data distribution shifts.
The cost and time associated with deploying and maintaining AI in dynamic environments significantly decrease due to reduced relabeling efforts.
This could accelerate the adoption of AI in critical real-world applications where data variability is a major challenge, such as medical diagnostics or industrial automation.
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Read at arXiv cs.LG