arXiv:2605.02439v3 Announce Type: replace-cross Abstract: Synthesizing realistic and diverse anomalous samples from limited data is vital for robust model generalization. However, existing methods struggle to reconcile fidelity and diversity, often hampered by distribution misalignment and overfitting, respectively.To mitigate this, we introduce Anomaly Preference Optimization,a novel paradigm that reformulates anomaly generation as a preference learning problem.Central to our approach is an implicit preference alignment mechanism that leverages real anomalies as positive references, deriving
The continuous push for more robust and generalized AI models, especially in critical applications, necessitates solving the challenge of generating diverse synthetic data from limited real samples.
Improving anomaly generation enhances model resilience and generalization, crucial for AI systems operating in high-stakes environments where real anomaly data is scarce and costly.
Traditional anomaly generation methods are often limited by fidelity and diversity issues; this new approach through preference learning could significantly improve the quality and utility of synthetic anomaly data.
- · AI safety and robustness researchers
- · Autonomous systems developers
- · Cybersecurity systems
- · Medical diagnostics
- · Systems highly reliant on manual anomaly detection
- · AI models without robust generalization capabilities
AI models will become more resilient to novel or rare anomalous events.
This could accelerate the deployment of AI in critical infrastructure and highly variable environments.
Reduced need for vast real-world anomaly datasets might reallocate R&D resources towards more sophisticated synthetic data generation techniques.
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Read at arXiv cs.LG