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Anomaly detection plays a critical role in intelligent video surveillance. However, real‐world video data obtained always contains large numbers of normal video data, along with large numbers of unlabeled data. A promising solution with one‐class classification and semi‐supervised learning may not be satisfactory as they fail to make good use of unlabeled data with only normal data available. In this...
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