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To challenge the traditional \"data quantity first\" convention, we propose a novel framework \"Beyond Augmentation\": Score-Guided Classification (SGC). SGC does not synthesize pseudo-samples; instead, it utilizes an unsupervised generative network architecture to model the structural and statistical anomaly degrees of samples, serving as the core \"Pathological Prior\". This prior, after robust normalization, is explicitly fused with deep feature representations, thereby precisely guiding the classifier's decision boundary. 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