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Advanced Deep Learning Methods., , , , , and . Bildverarbeitung für die Medizin, page 6. Springer Vieweg, (2018)High- and Low-level image component decomposition using VAEs for improved reconstruction and anomaly detection., , and . CoRR, (2019)Abstract: Unsupervised Anomaly Localization Using Variational Auto-Encoders., , , , and . Bildverarbeitung für die Medizin, page 199. Springer, (2020)Abstract: RecycleNet - Latent Feature Recycling Leads to Iterative Decision Refinement., , , , , , , , and . Bildverarbeitung für die Medizin, page 219. Springer, (2024)CRADL: Contrastive Representations for Unsupervised Anomaly Detection and Localization., , , , , , and . CoRR, (2023)GP-ConvCNP: Better Generalization for Convolutional Conditional Neural Processes on Time Series Data., , , , , and . CoRR, (2021)Abstract: nnU-Net: Self-adapting Framework for U-Net-Based Medical Image Segmentation., , , , , , , , , and 1 other author(s). Bildverarbeitung für die Medizin, page 22. Springer Vieweg, (2019)A Case for the Score: Identifying Image Anomalies using Variational Autoencoder Gradients., , , and . CoRR, (2019)Robust environment perception for the Audi Autonomous Driving Cup., , , , , , , and . ITSC, page 1424-1431. IEEE, (2016)SAM.MD: Zero-shot medical image segmentation capabilities of the Segment Anything Model., , , , , , , and . CoRR, (2023)