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News/Anomaly-Preference Image Generation
arxiv
PublishedMay 5, 2026 at 4:00 AM
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Anomaly-Preference Image Generation

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Publisher summary· verbatim

arXiv:2605.02439v1 Announce Type: 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, resp

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#anomaly-detection#machine-learning#computer-vision#generative-models

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arxiv
Read original ↗All from arxiv →
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#anomaly-detection#machine-learning#computer-vision#generative-models

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