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A Dual-evidence framework with adaptive Fusion and Aggregation (DiFA) for token-level anomaly detection, which derives anomaly scores from form-structural and semantic views to capture visible structural abnormality and contextual inconsistency, thereby providing complementary evidence for identifying diverse anomalies...
A hard pseudo-anomaly generation module to construct pseudo-anomalous tokens, simulating the subtle anomalies obscured by semantic smoothing, and a probabilistic boundary loss is developed to standardize anomaly scores into statistical distances, effectively enforcing anomalous instances to deviate significantly from t...
This paper proposes a novel Partial label-based Self-training framework (PaSta) that leverages partial label learning technique to overcome the limitations of existing methods and designs a partial label-based classification model with two well-crafted loss functions to guide the model learning at both label and repres...
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