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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)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)Learning Adversarial Semantic Embeddings for Zero-Shot Recognition in Open Worlds., , , , , and . CoRR, (2023)Residual Pattern Learning for Pixel-wise Out-of-Distribution Detection in Semantic Segmentation., , , , , , and . CoRR, (2022)LARA: A Light and Anti-overfitting Retraining Approach for Unsupervised Anomaly Detection., , , , , , and . CoRR, (2023)Deep Reinforcement Learning for Unknown Anomaly Detection., , , and . CoRR, (2020)Joint hyperbolic and Euclidean geometry contrastive graph neural networks., , , and . Inf. Sci., (2022)Feature Prediction Diffusion Model for Video Anomaly Detection., , , , and . ICCV, page 5504-5514. IEEE, (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)