arXiv:2605.24631v1 Announce Type: new Abstract: Minority sampling aims to generate low-density instances on a data manifold and is of central importance in applications such as medical diagnosis, anomaly detection, and creative AI. Existing approaches, however, define minority samples relative to generative priors learned from training data, confining rarity to model-specific notions that may poorly reflect real-world semantics. In this work, we propose a world-centric perspective on minority sampling, which defines rarity with respect to real-world priors rather than generator-induced densiti
The paper addresses current limitations in AI's ability to generate data that truly reflects real-world rarity, moving beyond mere generative priors.
This research could significantly improve the reliability and applicability of AI in critical fields like medical diagnosis and anomaly detection by basing rarity on real-world semantics.
AI systems will be able to perform minority sampling based on 'world-centric' perspectives, potentially leading to more robust and less biased AI applications.
- · Medical diagnosis AI developers
- · Anomaly detection software providers
- · Creative AI platforms
- · Sectors requiring high-fidelity synthetic data
- · AI models relying solely on generative priors for rarity
- · Systems with poor minority class representation
- · Applications with high false positive rates due to sampling bias
Improved accuracy and trustworthiness of AI systems in identifying rare but critical conditions or events.
Accelerated adoption of AI in highly sensitive domains where current methods are insufficient due to data scarcity challenges.
Potentially democratized access to advanced AI capabilities for challenges previously limited by the availability of diverse, real-world minority data.
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Read at arXiv cs.LG