conftrace_
← Learning Types
Deep Learning › Learning Types ›

Anomaly Detection

14 papers

Papers per year

1
1
3
3
1
4
1

Papers

Training-Free Zero-Shot Anomaly Detection in 3D Brain MRI with 2D Foundation Models MIDL 2026
Dynamic Neighborhood Modeling via Node-Subgraph Contrastive Learning for Graph-Based Fraud Detection AAAI 2025
Graph Mixture of Experts and Memory-augmented Routers for Multivariate Time Series Anomaly Detection AAAI 2025
Where's the Liability in the Generative Era? Recovery-based Black-Box Detection of AI-Generated Content CVPR 2025
A knowledge-based method for detecting network-induced shape artifacts in synthetic images MIDL 2025
EGonc : Energy-based Open-Set Node Classification with substitute Unknowns NIPS 2024
PAD: A Dataset and Benchmark for Pose-agnostic Anomaly Detection NIPS 2023
SimpleNet: A Simple Network for Image Anomaly Detection and Localization CVPR 2023
Normalizing Flow Based Feature Synthesis for Outlier-Aware Object Detection CVPR 2023
Deep One-Class Classification via Interpolated Gaussian Descriptor AAAI 2022
Anomaly Detection via Reverse Distillation From One-Class Embedding CVPR 2022
Towards Total Recall in Industrial Anomaly Detection CVPR 2022
Unsupervised Anomaly Detection in Parole Hearings using Language Models EMNLP 2020
Deep Anomaly Detection Using Geometric Transformations NIPS 2018