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ZERO++: Harnessing the Power of Zero Appearances to Detect Anomalies in Large-Scale Data Sets., , , and . J. Artif. Intell. Res., (2016)Weakly-supervised Deep Anomaly Detection with Pairwise Relation Learning., , and . CoRR, (2019)Deep Reinforcement Learning for Unknown Anomaly Detection., , , and . CoRR, (2020)LARA: A Light and Anti-overfitting Retraining Approach for Unsupervised Anomaly Detection., , , , , , and . CoRR, (2023)Learning transferable deep convolutional neural networks for the classification of bacterial virulence factors., , , , and . Bioinform., 36 (12): 3693-3702 (2020)Beyond Triplet Loss: Person Re-Identification With Fine-Grained Difference-Aware Pairwise Loss., , , , , , and . IEEE Trans. Multim., (2022)Residual Pattern Learning for Pixel-wise Out-of-Distribution Detection in Semantic Segmentation., , , , , , and . CoRR, (2022)Learning Adversarial Semantic Embeddings for Zero-Shot Recognition in Open Worlds., , , , , and . CoRR, (2023)Graph-Level Anomaly Detection via Hierarchical Memory Networks., , and . ECML/PKDD (1), volume 14169 of Lecture Notes in Computer Science, page 201-218. Springer, (2023)ANDEA: Anomaly and Novelty Detection, Explanation, and Accommodation., , , , and . KDD, page 4892-4893. ACM, (2022)