Abstract: Industrial few-shot anomaly detection (FSAD) requires identifying various abnormal states by leveraging as few normal samples as possible (abnormal samples are unavailable during training).
New benchmarks show semantic code graphs helping coding agents find change locations faster and complete updates more ...
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