AI RESEARCH
Anomaly-Preference Image Generation
arXiv CS.LG
•
ArXi:2605.02439v1 Announce Type: cross 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