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Synthesizing training data with foundation models and attending to wavelet domains can dramatically boost anomaly detection, even without fine-tuning or class-specific training.
Achieve superior single image reflection removal by closing the semantic gap between pre-trained models and reflection removal models, and unifying reflection labels across synthetic and real-world data.