{"total":19,"rows":[{"id":"2412.03342","title":"UniVAD: A Training-free Unified Model for Few-shot Visual Anomaly Detection","created":"2024-12-04","cited_by_count":28,"area":null},{"id":"2404.04518","title":"MedIAnomaly: A comparative study of anomaly detection in medical images","created":"2024-04-06","cited_by_count":26,"area":null},{"id":"2501.13864","title":"Autoencoders for Anomaly Detection are Unreliable","created":"2025-01-23","cited_by_count":9,"area":null},{"id":"2409.17485","title":"Revisiting Deep Ensemble Uncertainty for Enhanced Medical Anomaly Detection","created":"2024-09-26","cited_by_count":4,"area":null},{"id":"2412.17586","title":"Enhancing Reconstruction-Based Out-of-Distribution Detection in Brain MRI with Model and Metric Ensembles","created":"2024-12-23","cited_by_count":4,"area":null},{"id":"2504.04918","title":"Constitution or Collapse? Exploring Constitutional AI with Llama 3-8B","created":"2025-04-07","cited_by_count":4,"area":null},{"id":"2507.18481","title":"Q-Former Autoencoder: A Modern Framework for Medical Anomaly Detection","created":"2025-07-24","cited_by_count":2,"area":null},{"id":"2602.15315","title":"Training-Free Zero-Shot Anomaly Detection in 3D Brain MRI with 2D Foundation Models","created":"2026-02-17","cited_by_count":2,"area":null},{"id":"2504.11781","title":"ACMamba: Fast Unsupervised Anomaly Detection via An Asymmetrical Consensus State Space Model","created":"2025-04-16","cited_by_count":1,"area":null},{"id":"2507.17486","title":"Unsupervised anomaly detection using Bayesian flow networks: application to brain FDG PET in the context of Alzheimer's disease","created":"2025-07-23","cited_by_count":1,"area":null},{"id":"2605.02918","title":"Mitigating the reconstruction-detection trade-off in VAE-based unsupervised anomaly detection","created":"2026-04-09","cited_by_count":1,"area":null},{"id":"2503.13400","title":"U2AD: Uncertainty-based Unsupervised Anomaly Detection Framework for Detecting T2 Hyperintensity in MRI Spinal Cord","created":"2025-03-17","cited_by_count":0,"area":null},{"id":"2508.21795","title":"TMUAD: Enhancing Logical Capabilities in Unified Anomaly Detection Models with a Text Memory Bank","created":"2025-08-29","cited_by_count":0,"area":null},{"id":"2510.18342","title":"ShortcutBreaker: Low-Rank Noisy Bottleneck and Frequency Filtering Block for Multi-Class Unsupervised Anomaly Detection","created":"2025-10-21","cited_by_count":0,"area":null},{"id":"2512.11284","title":"RcAE: Recursive Reconstruction Framework for Unsupervised Industrial Anomaly Detection","created":"2025-12-12","cited_by_count":0,"area":null},{"id":"2606.18749","title":"Toward Training-Free Zero-Shot Anomaly Detection in 3D Medical Images: A Batch-Based Approach Using 2D Foundation Models","created":"2026-06-17","cited_by_count":0,"area":null},{"id":"2606.29428","title":"Robust Zero-shot Anomaly Detection under Limited Auxiliary Anomaly Priors","created":"2026-06-28","cited_by_count":0,"area":null},{"id":"2608.26724","title":"GeoMAD: Geometry-Aware Multi-View Anomaly Detection via Deformable Fusion and Distributional Alignment","created":"2026-08-27","cited_by_count":0,"area":null},{"id":"2610.09670","title":"Quasi-Binarized Autoencoders: An Architecture-Independent Information Bottleneck for Medical Image Anomaly Detection","created":"2026-10-07","cited_by_count":0,"area":null}],"next_cursor":null}